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tig-algorithms/src/hypergraph/freud_opt/README.md Normal file
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# TIG Code Submission
## Submission Details
* **Challenge Name:** hypergraph
* **Algorithm Name:** freud_opt
* **Copyright:** 2025 ChervovNikita
* **Identity of Submitter:** ChervovNikita
* **Identity of Creator of Algorithmic Method:** null
* **Unique Algorithm Identifier (UAI):** null
## License
The files in this folder are under the following licenses:
* TIG Benchmarker Outbound License
* TIG Commercial License
* TIG Inbound Game License
* TIG Innovator Outbound Game License
* TIG Open Data License
* TIG THV Game License
Copies of the licenses can be obtained at:
https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses

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tig-algorithms/src/hypergraph/freud_opt/freud_opt.cu Normal file
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#include <stdint.h>
#include <cuda_runtime.h>
extern "C" __global__ void hyperedge_clustering(
const int num_hyperedges,
const int num_clusters,
const int *hyperedge_offsets,
int *hyperedge_clusters
) {
int hedge = blockIdx.x * blockDim.x + threadIdx.x;
if (hedge < num_hyperedges) {
int start = hyperedge_offsets[hedge];
int end = hyperedge_offsets[hedge + 1];
int hedge_size = end - start;
int quarter_clusters = num_clusters >> 2;
int cluster_mask = quarter_clusters - 1;
int bucket = (hedge_size > 8) ? 3 :
(hedge_size > 4) ? 2 :
(hedge_size > 2) ? 1 : 0;
int cluster = bucket * quarter_clusters + (hedge & cluster_mask);
hyperedge_clusters[hedge] = cluster;
}
}
extern "C" __global__ void compute_node_preferences(
const int num_nodes,
const int num_parts,
const int num_hedge_clusters,
const int *node_hyperedges,
const int *node_offsets,
const int *hyperedge_clusters,
const int *hyperedge_offsets,
int *pref_parts,
int *pref_priorities
) {
int node = blockIdx.x * blockDim.x + threadIdx.x;
if (node < num_nodes) {
int start = node_offsets[node];
int end = node_offsets[node + 1];
int node_degree = end - start;
int cluster_votes[64];
int max_clusters = min(num_hedge_clusters, 64);
for (int i = 0; i < max_clusters; i++) {
cluster_votes[i] = 0;
}
int max_votes = 0;
int best_cluster = 0;
for (int j = start; j < end; j++) {
int hyperedge = node_hyperedges[j];
int cluster = hyperedge_clusters[hyperedge];
if (cluster >= 0 && cluster < max_clusters) {
int hedge_start = hyperedge_offsets[hyperedge];
int hedge_end = hyperedge_offsets[hyperedge + 1];
int hedge_size = hedge_end - hedge_start;
int weight = (hedge_size <= 2) ? 6 :
(hedge_size <= 4) ? 4 :
(hedge_size <= 8) ? 2 : 1;
cluster_votes[cluster] += weight;
if (cluster_votes[cluster] > max_votes ||
(cluster_votes[cluster] == max_votes && cluster < best_cluster)) {
max_votes = cluster_votes[cluster];
best_cluster = cluster;
}
}
}
int base_part = (num_parts > 0) ? (best_cluster % num_parts) : 0;
int target_partition = base_part;
pref_parts[node] = target_partition;
int degree_weight = node_degree > 255 ? 255 : node_degree;
pref_priorities[node] = (max_votes << 16) + (degree_weight << 8) + (num_parts - (node % num_parts));
}
}
extern "C" __global__ void execute_node_assignments(
const int num_nodes,
const int num_parts,
const int max_part_size,
const int *sorted_nodes,
const int *sorted_parts,
int *partition,
int *nodes_in_part
) {
if (blockIdx.x == 0 && threadIdx.x == 0) {
for (int i = 0; i < num_nodes; i++) {
int node = sorted_nodes[i];
int preferred_part = sorted_parts[i];
if (node >= 0 && node < num_nodes && preferred_part >= 0 && preferred_part < num_parts) {
bool assigned = false;
for (int attempt = 0; attempt < num_parts; attempt++) {
int try_part = (preferred_part + attempt) % num_parts;
if (nodes_in_part[try_part] < max_part_size) {
partition[node] = try_part;
nodes_in_part[try_part]++;
assigned = true;
break;
}
}
if (!assigned) {
int fallback_part = node % num_parts;
partition[node] = fallback_part;
nodes_in_part[fallback_part]++;
}
}
}
}
}
extern "C" __global__ void precompute_edge_flags(
const int num_hyperedges,
const int num_nodes,
const int *hyperedge_nodes,
const int *hyperedge_offsets,
const int *partition,
unsigned long long *edge_flags_all,
unsigned long long *edge_flags_double
) {
int hedge = blockIdx.x * blockDim.x + threadIdx.x;
if (hedge < num_hyperedges) {
int start = hyperedge_offsets[hedge];
int end = hyperedge_offsets[hedge + 1];
unsigned long long flags_all = 0;
unsigned long long flags_double = 0;
for (int k = start; k < end; k++) {
int node = hyperedge_nodes[k];
if (node >= 0 && node < num_nodes) {
int part = partition[node];
if (part >= 0 && part < 64) {
unsigned long long bit = 1ULL << part;
flags_double |= (flags_all & bit);
flags_all |= bit;
}
}
}
edge_flags_all[hedge] = flags_all;
edge_flags_double[hedge] = flags_double;
}
}
extern "C" __global__ void compute_refinement_moves(
const int num_nodes,
const int num_parts,
const int max_part_size,
const int *node_hyperedges,
const int *node_offsets,
const int *partition,
const int *nodes_in_part,
const unsigned long long *edge_flags_all,
const unsigned long long *edge_flags_double,
int *move_parts,
int *move_priorities,
int *num_valid_moves,
unsigned long long *global_edge_flags
) {
int node = blockIdx.x * blockDim.x + threadIdx.x;
if (node < num_nodes) {
move_parts[node] = partition[node];
move_priorities[node] = 0;
int current_part = partition[node];
if (current_part < 0 || current_part >= num_parts || nodes_in_part[current_part] <= 1) return;
int start = node_offsets[node];
int end = node_offsets[node + 1];
int node_degree = end - start;
int degree_weight = node_degree > 255 ? 255 : node_degree;
int used_degree = node_degree > 1024 ? 1024 : node_degree;
unsigned long long *edge_flags = &global_edge_flags[node * 1024];
unsigned long long cur_node_bit = 1ULL << current_part;
for (int j = 0; j < used_degree; j++) {
int rel = (int)(((long long)j * node_degree) / used_degree);
int hyperedge = node_hyperedges[start + rel];
unsigned long long flags_all = edge_flags_all[hyperedge];
unsigned long long flags_double = edge_flags_double[hyperedge];
edge_flags[j] = (flags_all & ~cur_node_bit) | (flags_double & cur_node_bit);
}
int original_cost = 0;
for (int j = 0; j < used_degree; j++) {
int lambda = __popcll(edge_flags[j] | cur_node_bit);
if (lambda > 1) {
original_cost += (lambda - 1);
}
}
int candidates[64];
int num_candidates = 0;
bool seen[64] = {false};
for (int j = 0; j < used_degree; j++) {
unsigned long long flags = edge_flags[j];
while (flags) {
int bit = __ffsll(flags) - 1;
flags &= ~(1ULL << bit);
if (bit != current_part && !seen[bit] && num_candidates < 64) {
candidates[num_candidates++] = bit;
seen[bit] = true;
}
}
}
int best_gain = 0;
int best_target = current_part;
for (int i = 0; i < num_candidates; i++) {
int target_part = candidates[i];
if (target_part < 0 || target_part >= num_parts) continue;
if (nodes_in_part[target_part] >= max_part_size) continue;
int new_cost = 0;
for (int j = 0; j < used_degree; j++) {
int lambda = __popcll(edge_flags[j] | (1ULL << target_part));
if (lambda > 1) {
new_cost += (lambda - 1);
}
}
int basic_gain = original_cost - new_cost;
int current_size = nodes_in_part[current_part];
int target_size = nodes_in_part[target_part];
int balance_bonus = 0;
if (current_size > target_size + 1) {
balance_bonus = 4;
}
int total_gain = basic_gain + balance_bonus;
if (total_gain > best_gain ||
(total_gain == best_gain && target_part < best_target)) {
best_gain = total_gain;
best_target = target_part;
}
}
if (best_gain > 0 && best_target != current_part) {
move_parts[node] = best_target;
move_priorities[node] = (best_gain << 16) + (degree_weight << 8) + (num_parts - (node % num_parts));
atomicAdd(num_valid_moves, 1);
}
}
}
extern "C" __global__ void execute_refinement_moves(
const int num_valid_moves,
const int *sorted_nodes,
const int *sorted_parts,
const int max_part_size,
int *partition,
int *nodes_in_part,
int *moves_executed
) {
if (blockIdx.x == 0 && threadIdx.x == 0) {
for (int i = 0; i < num_valid_moves; i++) {
int node = sorted_nodes[i];
int target_part = sorted_parts[i];
if (node >= 0 && target_part >= 0) {
int current_part = partition[node];
if (current_part >= 0 &&
nodes_in_part[target_part] < max_part_size &&
nodes_in_part[current_part] > 1 &&
partition[node] == current_part) {
partition[node] = target_part;
nodes_in_part[current_part]--;
nodes_in_part[target_part]++;
(*moves_executed)++;
}
}
}
}
}
extern "C" __global__ void radix_histogram_chunked(
const int n,
const int num_chunks,
const int *keys,
const int shift,
int *chunk_histograms
) {
int chunk = blockIdx.x;
if (chunk >= num_chunks) return;
__shared__ int local_hist[256];
for (int i = threadIdx.x; i < 256; i += blockDim.x) {
local_hist[i] = 0;
}
__syncthreads();
int chunk_start = chunk * 256;
int chunk_end = min(chunk_start + 256, n);
for (int i = chunk_start + threadIdx.x; i < chunk_end; i += blockDim.x) {
int digit = (keys[i] >> shift) & 0xFF;
atomicAdd(&local_hist[digit], 1);
}
__syncthreads();
for (int d = threadIdx.x; d < 256; d += blockDim.x) {
chunk_histograms[chunk * 256 + d] = local_hist[d];
}
}
extern "C" __global__ void radix_prefix_and_scatter(
const int n,
const int num_chunks,
const int *keys_in,
const int *vals_in,
const int shift,
const int *chunk_histograms,
int *chunk_offsets,
int *keys_out,
int *vals_out,
int *ready_flag
) {
if (blockIdx.x == 0 && threadIdx.x == 0) {
int digit_totals[256];
for (int d = 0; d < 256; d++) {
digit_totals[d] = 0;
for (int c = 0; c < num_chunks; c++) {
digit_totals[d] += chunk_histograms[c * 256 + d];
}
}
int digit_starts[256];
int sum = 0;
for (int d = 0; d < 256; d++) {
digit_starts[d] = sum;
sum += digit_totals[d];
}
int running[256];
for (int d = 0; d < 256; d++) running[d] = digit_starts[d];
for (int c = 0; c < num_chunks; c++) {
for (int d = 0; d < 256; d++) {
chunk_offsets[c * 256 + d] = running[d];
running[d] += chunk_histograms[c * 256 + d];
}
}
__threadfence();
atomicExch(ready_flag, 1);
}
if (threadIdx.x == 0) {
while (atomicAdd(ready_flag, 0) == 0) {}
}
__syncthreads();
int chunk = blockIdx.x;
if (chunk >= num_chunks) return;
__shared__ int offsets[256];
for (int d = threadIdx.x; d < 256; d += blockDim.x) {
offsets[d] = chunk_offsets[chunk * 256 + d];
}
__syncthreads();
int chunk_start = chunk * 256;
int chunk_end = min(chunk_start + 256, n);
if (threadIdx.x == 0) {
for (int i = chunk_start; i < chunk_end; i++) {
int key = keys_in[i];
int digit = (key >> shift) & 0xFF;
int pos = offsets[digit]++;
keys_out[pos] = key;
vals_out[pos] = vals_in[i];
}
}
}
extern "C" __global__ void init_indices(
const int n,
int *indices
) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
indices[i] = i;
}
}
extern "C" __global__ void invert_keys(
const int n,
const int max_key,
const int *keys_in,
int *keys_out
) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
keys_out[i] = max_key - keys_in[i];
}
}
extern "C" __global__ void gather_sorted(
const int n,
const int *sorted_indices,
const int *src,
int *dst
) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
dst[i] = src[sorted_indices[i]];
}
}
extern "C" __global__ void balance_final(
const int num_nodes,
const int num_parts,
const int min_part_size,
const int max_part_size,
int *partition,
int *nodes_in_part
) {
if (blockIdx.x == 0 && threadIdx.x == 0) {
for (int part = 0; part < num_parts; part++) {
while (nodes_in_part[part] < min_part_size) {
bool moved = false;
for (int other_part = 0; other_part < num_parts && !moved; other_part++) {
if (other_part != part && nodes_in_part[other_part] > min_part_size) {
for (int node = 0; node < num_nodes; node++) {
if (partition[node] == other_part) {
partition[node] = part;
nodes_in_part[other_part]--;
nodes_in_part[part]++;
moved = true;
break;
}
}
}
}
if (!moved) break;
}
}
for (int part = 0; part < num_parts; part++) {
while (nodes_in_part[part] > max_part_size) {
bool moved = false;
for (int other_part = 0; other_part < num_parts && !moved; other_part++) {
if (other_part != part && nodes_in_part[other_part] < max_part_size) {
for (int node = 0; node < num_nodes; node++) {
if (partition[node] == part) {
partition[node] = other_part;
nodes_in_part[part]--;
nodes_in_part[other_part]++;
moved = true;
break;
}
}
}
}
if (!moved) break;
}
}
}
}

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use cudarc::{
driver::{safe::LaunchConfig, CudaModule, CudaStream, PushKernelArg},
runtime::sys::cudaDeviceProp,
};
use std::sync::Arc;
use std::time::Instant;
use serde_json::{Map, Value};
use tig_challenges::hypergraph::*;
pub fn help() {
println!("Hypergraph Partitioning Algorithm");
println!("Adaptive clustering with GPU-accelerated refinement");
println!();
println!("Hyperparameters:");
println!(" refinement - Number of refinement rounds (default: 500, range: 50-5000)");
println!();
println!("Usage:");
println!(" Set the 'refinement' parameter in your benchmarker config");
println!(" to balance between solution quality and runtime.");
}
pub fn solve_challenge(
challenge: &Challenge,
save_solution: &dyn Fn(&Solution) -> anyhow::Result<()>,
hyperparameters: &Option<Map<String, Value>>,
module: Arc<CudaModule>,
stream: Arc<CudaStream>,
prop: &cudaDeviceProp,
) -> anyhow::Result<()> {
println!(">>> solve_challenge START");
let total_start = Instant::now();
let dummy_partition: Vec<u32> = (0..challenge.num_nodes as u32)
.map(|i| i % challenge.num_parts as u32)
.collect();
save_solution(&Solution { partition: dummy_partition })?;
let block_size = std::cmp::min(128, prop.maxThreadsPerBlock as u32);
let t_load = Instant::now();
let hyperedge_cluster_kernel = module.load_function("hyperedge_clustering")?;
let compute_preferences_kernel = module.load_function("compute_node_preferences")?;
let execute_assignments_kernel = module.load_function("execute_node_assignments")?;
let precompute_edge_flags_kernel = module.load_function("precompute_edge_flags")?;
let compute_moves_kernel = module.load_function("compute_refinement_moves")?;
let execute_moves_kernel = module.load_function("execute_refinement_moves")?;
let balance_kernel = module.load_function("balance_final")?;
let radix_hist_kernel = module.load_function("radix_histogram_chunked")?;
let radix_prefix_scatter_kernel = module.load_function("radix_prefix_and_scatter")?;
let init_indices_kernel = module.load_function("init_indices")?;
let invert_keys_kernel = module.load_function("invert_keys")?;
let gather_sorted_kernel = module.load_function("gather_sorted")?;
let t_load_elapsed = t_load.elapsed();
let cfg = LaunchConfig {
grid_dim: ((challenge.num_nodes as u32 + block_size - 1) / block_size, 1, 1),
block_dim: (block_size, 1, 1),
shared_mem_bytes: 0,
};
let one_thread_cfg = LaunchConfig {
grid_dim: (1, 1, 1),
block_dim: (1, 1, 1),
shared_mem_bytes: 0,
};
let hedge_cfg = LaunchConfig {
grid_dim: ((challenge.num_hyperedges as u32 + block_size - 1) / block_size, 1, 1),
block_dim: (block_size, 1, 1),
shared_mem_bytes: 0,
};
let mut num_hedge_clusters = 64;
let t_alloc = Instant::now();
let mut d_hyperedge_clusters = stream.alloc_zeros::<i32>(challenge.num_hyperedges as usize)?;
let mut d_partition = stream.alloc_zeros::<i32>(challenge.num_nodes as usize)?;
let mut d_nodes_in_part = stream.alloc_zeros::<i32>(challenge.num_parts as usize)?;
let mut d_pref_parts = stream.alloc_zeros::<i32>(challenge.num_nodes as usize)?;
let mut d_pref_priorities = stream.alloc_zeros::<i32>(challenge.num_nodes as usize)?;
let mut d_move_parts = stream.alloc_zeros::<i32>(challenge.num_nodes as usize)?;
let mut d_move_priorities = stream.alloc_zeros::<i32>(challenge.num_nodes as usize)?;
let buffer_size = (challenge.num_nodes as usize) * 1024;
let mut d_global_edge_flags = stream.alloc_zeros::<u64>(buffer_size)?;
let mut d_edge_flags_all = stream.alloc_zeros::<u64>(challenge.num_hyperedges as usize)?;
let mut d_edge_flags_double = stream.alloc_zeros::<u64>(challenge.num_hyperedges as usize)?;
let n = challenge.num_nodes as usize;
let mut d_sort_keys_a = stream.alloc_zeros::<i32>(n)?;
let mut d_sort_keys_b = stream.alloc_zeros::<i32>(n)?;
let mut d_sort_vals_a = stream.alloc_zeros::<i32>(n)?;
let mut d_sort_vals_b = stream.alloc_zeros::<i32>(n)?;
let mut d_sorted_move_parts = stream.alloc_zeros::<i32>(n)?;
let num_chunks: i32 = ((n + 255) / 256) as i32;
let mut d_chunk_histograms = stream.alloc_zeros::<i32>((num_chunks as usize) * 256)?;
let mut d_chunk_offsets = stream.alloc_zeros::<i32>((num_chunks as usize) * 256)?;
let mut d_ready_flag = stream.alloc_zeros::<i32>(1)?;
let t_alloc_elapsed = t_alloc.elapsed();
let radix_cfg = LaunchConfig {
grid_dim: (num_chunks as u32, 1, 1),
block_dim: (256, 1, 1),
shared_mem_bytes: 0,
};
let mut sorted_move_nodes: Vec<i32> = Vec::with_capacity(n);
let mut sorted_move_parts_cpu: Vec<i32> = Vec::with_capacity(n);
let mut valid_indices: Vec<usize> = Vec::with_capacity(n);
let default_refinement = if challenge.num_hyperedges < 20_000 {
400usize
} else {
500usize
};
println!("refinement: {:?}", hyperparameters.as_ref().and_then(|p| p.get("refinement")));
let refinement_rounds = if let Some(params) = hyperparameters {
params.get("refinement")
.and_then(|v| v.as_i64())
.map(|v| v.clamp(50, 5000) as usize)
.unwrap_or(default_refinement)
} else {
default_refinement
};
let t_init = Instant::now();
unsafe {
stream.launch_builder(&hyperedge_cluster_kernel)
.arg(&(challenge.num_hyperedges as i32))
.arg(&(num_hedge_clusters as i32))
.arg(&challenge.d_hyperedge_offsets)
.arg(&mut d_hyperedge_clusters)
.launch(LaunchConfig {
grid_dim: ((challenge.num_hyperedges as u32 + block_size - 1) / block_size, 1, 1),
block_dim: (block_size, 1, 1),
shared_mem_bytes: 0,
})?;
}
unsafe {
stream.launch_builder(&compute_preferences_kernel)
.arg(&(challenge.num_nodes as i32))
.arg(&(challenge.num_parts as i32))
.arg(&(num_hedge_clusters as i32))
.arg(&challenge.d_node_hyperedges)
.arg(&challenge.d_node_offsets)
.arg(&d_hyperedge_clusters)
.arg(&challenge.d_hyperedge_offsets)
.arg(&mut d_pref_parts)
.arg(&mut d_pref_priorities)
.launch(cfg.clone())?;
}
stream.synchronize()?;
let pref_parts = stream.memcpy_dtov(&d_pref_parts)?;
let pref_priorities = stream.memcpy_dtov(&d_pref_priorities)?;
let mut indices: Vec<usize> = (0..challenge.num_nodes as usize).collect();
indices.sort_unstable_by(|&a, &b| pref_priorities[b].cmp(&pref_priorities[a]));
let sorted_nodes: Vec<i32> = indices.iter().map(|&i| i as i32).collect();
let sorted_parts: Vec<i32> = indices.iter().map(|&i| pref_parts[i]).collect();
let d_sorted_nodes = stream.memcpy_stod(&sorted_nodes)?;
let d_sorted_parts = stream.memcpy_stod(&sorted_parts)?;
unsafe {
stream.launch_builder(&execute_assignments_kernel)
.arg(&(challenge.num_nodes as i32))
.arg(&(challenge.num_parts as i32))
.arg(&(challenge.max_part_size as i32))
.arg(&d_sorted_nodes)
.arg(&d_sorted_parts)
.arg(&mut d_partition)
.arg(&mut d_nodes_in_part)
.launch(one_thread_cfg.clone())?;
}
stream.synchronize()?;
let t_init_elapsed = t_init.elapsed();
let mut stagnant_rounds = 0;
let early_exit_round = if challenge.num_hyperedges < 20_000 { 90 } else { 70 };
let max_stagnant_rounds = if challenge.num_hyperedges < 20_000 { 30 } else { 20 };
let t_refine1 = Instant::now();
let mut t_gpu_kernels = 0u128;
let mut t_gpu_sort = 0u128;
let mut t_cpu_sort = 0u128;
let mut t_execute = 0u128;
let mut actual_rounds = 0usize;
let mut gpu_sort_count = 0usize;
let mut cpu_sort_count = 0usize;
for round in 0..refinement_rounds {
actual_rounds = round + 1;
let zero = vec![0i32];
let mut d_num_valid_moves = stream.memcpy_stod(&zero)?;
let t0 = Instant::now();
unsafe {
stream.launch_builder(&precompute_edge_flags_kernel)
.arg(&(challenge.num_hyperedges as i32))
.arg(&(challenge.num_nodes as i32))
.arg(&challenge.d_hyperedge_nodes)
.arg(&challenge.d_hyperedge_offsets)
.arg(&d_partition)
.arg(&mut d_edge_flags_all)
.arg(&mut d_edge_flags_double)
.launch(hedge_cfg.clone())?;
}
unsafe {
stream.launch_builder(&compute_moves_kernel)
.arg(&(challenge.num_nodes as i32))
.arg(&(challenge.num_parts as i32))
.arg(&(challenge.max_part_size as i32))
.arg(&challenge.d_node_hyperedges)
.arg(&challenge.d_node_offsets)
.arg(&d_partition)
.arg(&d_nodes_in_part)
.arg(&d_edge_flags_all)
.arg(&d_edge_flags_double)
.arg(&mut d_move_parts)
.arg(&mut d_move_priorities)
.arg(&mut d_num_valid_moves)
.arg(&mut d_global_edge_flags)
.launch(cfg.clone())?;
}
stream.synchronize()?;
t_gpu_kernels += t0.elapsed().as_micros();
let num_valid_moves = stream.memcpy_dtov(&d_num_valid_moves)?[0];
if num_valid_moves == 0 {
break;
}
let t2 = Instant::now();
let move_priorities_vec = stream.memcpy_dtov(&d_move_priorities)?;
let max_priority = move_priorities_vec.iter().copied().max().unwrap_or(0);
let num_passes = if max_priority == 0 {
0
} else if max_priority < 256 {
1
} else if max_priority < 65536 {
2
} else if max_priority < 16777216 {
3
} else {
4
};
let use_gpu_sort = num_passes > 0 && num_passes <= 3;
let (d_sorted_nodes_ref, d_sorted_parts_ref): (&cudarc::driver::CudaSlice<i32>, &cudarc::driver::CudaSlice<i32>);
let d_sorted_nodes_tmp: cudarc::driver::CudaSlice<i32>;
let d_sorted_parts_tmp: cudarc::driver::CudaSlice<i32>;
let num_to_process: i32;
if use_gpu_sort {
unsafe {
stream.launch_builder(&invert_keys_kernel)
.arg(&(n as i32))
.arg(&max_priority)
.arg(&d_move_priorities)
.arg(&mut d_sort_keys_a)
.launch(cfg.clone())?;
stream.launch_builder(&init_indices_kernel)
.arg(&(n as i32))
.arg(&mut d_sort_vals_a)
.launch(cfg.clone())?;
}
for pass in 0..num_passes {
let shift = pass * 8;
stream.memset_zeros(&mut d_ready_flag)?;
if pass % 2 == 0 {
unsafe {
stream.launch_builder(&radix_hist_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_a)
.arg(&shift)
.arg(&mut d_chunk_histograms)
.launch(radix_cfg.clone())?;
stream.launch_builder(&radix_prefix_scatter_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_a)
.arg(&d_sort_vals_a)
.arg(&shift)
.arg(&d_chunk_histograms)
.arg(&mut d_chunk_offsets)
.arg(&mut d_sort_keys_b)
.arg(&mut d_sort_vals_b)
.arg(&mut d_ready_flag)
.launch(radix_cfg.clone())?;
}
} else {
unsafe {
stream.launch_builder(&radix_hist_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_b)
.arg(&shift)
.arg(&mut d_chunk_histograms)
.launch(radix_cfg.clone())?;
stream.launch_builder(&radix_prefix_scatter_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_b)
.arg(&d_sort_vals_b)
.arg(&shift)
.arg(&d_chunk_histograms)
.arg(&mut d_chunk_offsets)
.arg(&mut d_sort_keys_a)
.arg(&mut d_sort_vals_a)
.arg(&mut d_ready_flag)
.launch(radix_cfg.clone())?;
}
}
}
let sorted_vals = if num_passes % 2 == 0 { &d_sort_vals_a } else { &d_sort_vals_b };
unsafe {
stream.launch_builder(&gather_sorted_kernel)
.arg(&(n as i32))
.arg(sorted_vals)
.arg(&d_move_parts)
.arg(&mut d_sorted_move_parts)
.launch(cfg.clone())?;
}
stream.synchronize()?;
d_sorted_nodes_ref = sorted_vals;
d_sorted_parts_ref = &d_sorted_move_parts;
num_to_process = n as i32;
t_gpu_sort += t2.elapsed().as_micros();
gpu_sort_count += 1;
} else {
let t_cpu = Instant::now();
let move_parts = stream.memcpy_dtov(&d_move_parts)?;
valid_indices.clear();
valid_indices.extend(
move_priorities_vec
.iter()
.enumerate()
.filter(|(_, &priority)| priority > 0)
.map(|(i, _)| i),
);
if valid_indices.is_empty() {
break;
}
valid_indices.sort_unstable_by(|&a, &b| move_priorities_vec[b].cmp(&move_priorities_vec[a]));
sorted_move_nodes.clear();
sorted_move_parts_cpu.clear();
sorted_move_nodes.extend(valid_indices.iter().map(|&i| i as i32));
sorted_move_parts_cpu.extend(valid_indices.iter().map(|&i| move_parts[i]));
d_sorted_nodes_tmp = stream.memcpy_stod(&sorted_move_nodes)?;
d_sorted_parts_tmp = stream.memcpy_stod(&sorted_move_parts_cpu)?;
d_sorted_nodes_ref = &d_sorted_nodes_tmp;
d_sorted_parts_ref = &d_sorted_parts_tmp;
num_to_process = sorted_move_nodes.len() as i32;
t_cpu_sort += t_cpu.elapsed().as_micros();
cpu_sort_count += 1;
}
let mut d_moves_executed = stream.alloc_zeros::<i32>(1)?;
let t4 = Instant::now();
unsafe {
stream.launch_builder(&execute_moves_kernel)
.arg(&num_to_process)
.arg(d_sorted_nodes_ref)
.arg(d_sorted_parts_ref)
.arg(&(challenge.max_part_size as i32))
.arg(&mut d_partition)
.arg(&mut d_nodes_in_part)
.arg(&mut d_moves_executed)
.launch(one_thread_cfg.clone())?;
}
stream.synchronize()?;
t_execute += t4.elapsed().as_micros();
let moves_executed = stream.memcpy_dtov(&d_moves_executed)?[0];
if moves_executed == 0 {
break;
}
if moves_executed == 1 && round > early_exit_round {
stagnant_rounds += 1;
if stagnant_rounds > max_stagnant_rounds {
break;
}
} else {
stagnant_rounds = 0;
}
}
let t_refine1_elapsed = t_refine1.elapsed();
let t_balance = Instant::now();
unsafe {
stream.launch_builder(&balance_kernel)
.arg(&(challenge.num_nodes as i32))
.arg(&(challenge.num_parts as i32))
.arg(&1)
.arg(&(challenge.max_part_size as i32))
.arg(&mut d_partition)
.arg(&mut d_nodes_in_part)
.launch(one_thread_cfg.clone())?;
}
stream.synchronize()?;
let t_balance_elapsed = t_balance.elapsed();
let t_refine2 = Instant::now();
for _ in 0..24 {
let zero = vec![0i32];
let mut d_num_valid_moves = stream.memcpy_stod(&zero)?;
unsafe {
stream.launch_builder(&precompute_edge_flags_kernel)
.arg(&(challenge.num_hyperedges as i32))
.arg(&(challenge.num_nodes as i32))
.arg(&challenge.d_hyperedge_nodes)
.arg(&challenge.d_hyperedge_offsets)
.arg(&d_partition)
.arg(&mut d_edge_flags_all)
.arg(&mut d_edge_flags_double)
.launch(hedge_cfg.clone())?;
}
unsafe {
stream.launch_builder(&compute_moves_kernel)
.arg(&(challenge.num_nodes as i32))
.arg(&(challenge.num_parts as i32))
.arg(&(challenge.max_part_size as i32))
.arg(&challenge.d_node_hyperedges)
.arg(&challenge.d_node_offsets)
.arg(&d_partition)
.arg(&d_nodes_in_part)
.arg(&d_edge_flags_all)
.arg(&d_edge_flags_double)
.arg(&mut d_move_parts)
.arg(&mut d_move_priorities)
.arg(&mut d_num_valid_moves)
.arg(&mut d_global_edge_flags)
.launch(cfg.clone())?;
}
stream.synchronize()?;
let num_valid_moves = stream.memcpy_dtov(&d_num_valid_moves)?[0];
if num_valid_moves == 0 {
break;
}
let move_priorities_vec2 = stream.memcpy_dtov(&d_move_priorities)?;
let max_priority2 = move_priorities_vec2.iter().copied().max().unwrap_or(0);
let num_passes2 = if max_priority2 == 0 {
0
} else if max_priority2 < 256 {
1
} else if max_priority2 < 65536 {
2
} else if max_priority2 < 16777216 {
3
} else {
4
};
let use_gpu_sort = num_passes2 > 0 && num_passes2 <= 3;
let d_sorted_nodes_ref2: &cudarc::driver::CudaSlice<i32>;
let d_sorted_parts_ref2: &cudarc::driver::CudaSlice<i32>;
let d_sorted_nodes_tmp2: cudarc::driver::CudaSlice<i32>;
let d_sorted_parts_tmp2: cudarc::driver::CudaSlice<i32>;
let num_to_process2: i32;
if use_gpu_sort {
unsafe {
stream.launch_builder(&invert_keys_kernel)
.arg(&(n as i32))
.arg(&max_priority2)
.arg(&d_move_priorities)
.arg(&mut d_sort_keys_a)
.launch(cfg.clone())?;
stream.launch_builder(&init_indices_kernel)
.arg(&(n as i32))
.arg(&mut d_sort_vals_a)
.launch(cfg.clone())?;
}
for pass in 0..num_passes2 {
let shift = pass * 8;
stream.memset_zeros(&mut d_ready_flag)?;
if pass % 2 == 0 {
unsafe {
stream.launch_builder(&radix_hist_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_a)
.arg(&shift)
.arg(&mut d_chunk_histograms)
.launch(radix_cfg.clone())?;
stream.launch_builder(&radix_prefix_scatter_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_a)
.arg(&d_sort_vals_a)
.arg(&shift)
.arg(&d_chunk_histograms)
.arg(&mut d_chunk_offsets)
.arg(&mut d_sort_keys_b)
.arg(&mut d_sort_vals_b)
.arg(&mut d_ready_flag)
.launch(radix_cfg.clone())?;
}
} else {
unsafe {
stream.launch_builder(&radix_hist_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_b)
.arg(&shift)
.arg(&mut d_chunk_histograms)
.launch(radix_cfg.clone())?;
stream.launch_builder(&radix_prefix_scatter_kernel)
.arg(&(n as i32))
.arg(&num_chunks)
.arg(&d_sort_keys_b)
.arg(&d_sort_vals_b)
.arg(&shift)
.arg(&d_chunk_histograms)
.arg(&mut d_chunk_offsets)
.arg(&mut d_sort_keys_a)
.arg(&mut d_sort_vals_a)
.arg(&mut d_ready_flag)
.launch(radix_cfg.clone())?;
}
}
}
let sorted_vals2 = if num_passes2 % 2 == 0 { &d_sort_vals_a } else { &d_sort_vals_b };
unsafe {
stream.launch_builder(&gather_sorted_kernel)
.arg(&(n as i32))
.arg(sorted_vals2)
.arg(&d_move_parts)
.arg(&mut d_sorted_move_parts)
.launch(cfg.clone())?;
}
stream.synchronize()?;
d_sorted_nodes_ref2 = sorted_vals2;
d_sorted_parts_ref2 = &d_sorted_move_parts;
num_to_process2 = n as i32;
} else {
let move_parts = stream.memcpy_dtov(&d_move_parts)?;
valid_indices.clear();
valid_indices.extend(
move_priorities_vec2
.iter()
.enumerate()
.filter(|(_, &priority)| priority > 0)
.map(|(i, _)| i),
);
if valid_indices.is_empty() {
break;
}
valid_indices.sort_unstable_by(|&a, &b| move_priorities_vec2[b].cmp(&move_priorities_vec2[a]));
sorted_move_nodes.clear();
sorted_move_parts_cpu.clear();
sorted_move_nodes.extend(valid_indices.iter().map(|&i| i as i32));
sorted_move_parts_cpu.extend(valid_indices.iter().map(|&i| move_parts[i]));
d_sorted_nodes_tmp2 = stream.memcpy_stod(&sorted_move_nodes)?;
d_sorted_parts_tmp2 = stream.memcpy_stod(&sorted_move_parts_cpu)?;
d_sorted_nodes_ref2 = &d_sorted_nodes_tmp2;
d_sorted_parts_ref2 = &d_sorted_parts_tmp2;
num_to_process2 = sorted_move_nodes.len() as i32;
}
let mut d_moves_executed = stream.alloc_zeros::<i32>(1)?;
unsafe {
stream.launch_builder(&execute_moves_kernel)
.arg(&num_to_process2)
.arg(d_sorted_nodes_ref2)
.arg(d_sorted_parts_ref2)
.arg(&(challenge.max_part_size as i32))
.arg(&mut d_partition)
.arg(&mut d_nodes_in_part)
.arg(&mut d_moves_executed)
.launch(one_thread_cfg.clone())?;
}
stream.synchronize()?;
let moves_executed = stream.memcpy_dtov(&d_moves_executed)?[0];
if moves_executed == 0 {
break;
}
}
let t_refine2_elapsed = t_refine2.elapsed();
let partition = stream.memcpy_dtov(&d_partition)?;
let partition_u32: Vec<u32> = partition.iter().map(|&x| x as u32).collect();
save_solution(&Solution { partition: partition_u32 })?;
let total_elapsed = total_start.elapsed();
println!("=== FULL PROFILING ===");
println!("load_function: {:.2}ms", t_load_elapsed.as_micros() as f64 / 1000.0);
println!("alloc_zeros: {:.2}ms", t_alloc_elapsed.as_micros() as f64 / 1000.0);
println!("init (cluster+assign): {:.2}ms", t_init_elapsed.as_micros() as f64 / 1000.0);
println!("refine1 ({} rounds): {:.2}ms", actual_rounds, t_refine1_elapsed.as_micros() as f64 / 1000.0);
println!(" - GPU kernels: {:.2}ms", t_gpu_kernels as f64 / 1000.0);
println!(" - GPU sort: {:.2}ms ({} times)", t_gpu_sort as f64 / 1000.0, gpu_sort_count);
println!(" - CPU sort: {:.2}ms ({} times)", t_cpu_sort as f64 / 1000.0, cpu_sort_count);
println!(" - execute_moves: {:.2}ms", t_execute as f64 / 1000.0);
println!("balance: {:.2}ms", t_balance_elapsed.as_micros() as f64 / 1000.0);
println!("refine2 (24 rounds): {:.2}ms", t_refine2_elapsed.as_micros() as f64 / 1000.0);
println!("TOTAL: {:.2}ms", total_elapsed.as_micros() as f64 / 1000.0);
println!(">>> solve_challenge END");
Ok(())
}

3
tig-algorithms/src/hypergraph/mod.rs
View File

@ -24,7 +24,8 @@ pub use sigma_freud as c005_a010;
// c005_a011
// c005_a012
pub mod freud_opt;
pub use freud_opt as c005_a012;
// c005_a013

23
tig-algorithms/src/vehicle_routing/fast_lane_v2/README.md Normal file
View File

@ -0,0 +1,23 @@
# TIG Code Submission
## Submission Details
* **Challenge Name:** vehicle_routing
* **Algorithm Name:** fast_lane_v2
* **Copyright:** 2026 testing
* **Identity of Submitter:** testing
* **Identity of Creator of Algorithmic Method:** null
* **Unique Algorithm Identifier (UAI):** null
## License
The files in this folder are under the following licenses:
* TIG Benchmarker Outbound License
* TIG Commercial License
* TIG Inbound Game License
* TIG Innovator Outbound Game License
* TIG Open Data License
* TIG THV Game License
Copies of the licenses can be obtained at:
https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses

112
tig-algorithms/src/vehicle_routing/fast_lane_v2/builder.rs Normal file
View File

@ -0,0 +1,112 @@
use super::instance::Instance;
use rand::rngs::SmallRng;
use rand::Rng;
pub struct Builder;
impl Builder {
pub fn build_routes(data: &Instance, rng: &mut SmallRng, randomize: bool) -> Vec<Vec<usize>> {
let mut routes = Vec::new();
let mut nodes: Vec<usize> = (1..data.nb_nodes).collect();
let n = nodes.len();
nodes.sort_by(|&a, &b| data.dm(0, a).cmp(&data.dm(0, b)));
if randomize {
let window = if n < 1000 { 10 } else { 5 };
for i in 0..(n - 1) {
nodes.swap(i, rng.gen_range(i + 1..=(i + window).min(n - 1)));
}
}
let mut available = vec![true; data.nb_nodes];
available[0] = false;
while let Some(node) = nodes.pop() {
if !available[node] {
continue;
}
available[node] = false;
let mut route = vec![0, node, 0];
let mut route_demand = data.demands[node];
while let Some((best_node, best_pos)) =
Self::find_best_insertion(&route, &nodes, &available, route_demand, data)
{
available[best_node] = false;
route_demand += data.demands[best_node];
route.insert(best_pos, best_node);
}
routes.push(route);
}
routes
}
fn find_best_insertion(
route: &Vec<usize>,
nodes: &Vec<usize>,
available: &Vec<bool>,
route_demand: i32,
data: &Instance,
) -> Option<(usize, usize)> {
let mut best_c2 = None;
let mut best = None;
for &insert_node in nodes.iter() {
if !available[insert_node] || route_demand + data.demands[insert_node] > data.max_capacity {
continue;
}
let mut curr_time = 0;
let mut curr_node = 0;
for pos in 1..route.len() {
let next_node = route[pos];
let new_arrival_time_insert_node =
data.start_tw[insert_node].max(curr_time + data.dm(curr_node, insert_node));
if new_arrival_time_insert_node > data.end_tw[insert_node] {
break;
}
let c11 = data.dm(curr_node, insert_node) + data.dm(insert_node, next_node) - data.dm(curr_node, next_node);
let c2 = data.dm(0, insert_node) - c11;
let c2_is_better = match best_c2 {
None => true,
Some(x) => c2 > x,
};
if c2_is_better
&& Self::is_feasible(
route,
insert_node,
new_arrival_time_insert_node + data.service_times[insert_node],
pos,
data,
)
{
best_c2 = Some(c2);
best = Some((insert_node, pos));
}
curr_time =
data.start_tw[next_node].max(curr_time + data.dm(curr_node, next_node)) + data.service_times[next_node];
curr_node = next_node;
}
}
best
}
fn is_feasible(route: &Vec<usize>, mut curr_node: usize, mut curr_time: i32, start_pos: usize, data: &Instance) -> bool {
for pos in start_pos..route.len() {
let next_node = route[pos];
curr_time += data.dm(curr_node, next_node);
if curr_time > data.end_tw[route[pos]] {
return false;
}
curr_time = curr_time.max(data.start_tw[next_node]) + data.service_times[next_node];
curr_node = next_node;
}
true
}
}

208
tig-algorithms/src/vehicle_routing/fast_lane_v2/config.rs Normal file
View File

@ -0,0 +1,208 @@
use serde::{Deserialize, Serialize};
use serde_json::{Map, Value};
#[derive(Serialize, Deserialize, Clone, Copy)]
pub struct Config {
pub exploration_level: usize,
pub allow_swap3: bool,
pub granularity: usize,
pub granularity2: usize,
pub penalty_tw: usize,
pub penalty_capa: usize,
pub target_ratio: f64,
pub max_it_noimprov: usize,
pub max_it_total: usize,
pub nb_it_adapt_penalties: usize,
pub nb_it_traces: usize,
pub mu: usize,
pub mu_start: usize,
pub lambda: usize,
pub nb_close: usize,
pub nb_elite: usize,
}
impl Config {
fn preset(exploration_level: usize, nb_nodes: usize) -> Self {
let p = if nb_nodes <= 700 {
20
} else if nb_nodes <= 1000 {
30
} else if nb_nodes <= 1200 {
50
} else if nb_nodes <= 1500 {
80
} else if nb_nodes <= 2000 {
150
} else if nb_nodes <= 3000 {
200
} else {
500
};
match exploration_level {
0 => Self {
exploration_level: 0,
allow_swap3: true,
granularity: 40,
granularity2: 20,
penalty_tw: p,
penalty_capa: p,
target_ratio: 0.2,
max_it_noimprov: 0,
max_it_total: 0,
nb_it_adapt_penalties: 100,
nb_it_traces: 100,
mu: 2,
mu_start: 1,
lambda: 1,
nb_close: 1,
nb_elite: 1,
},
1 => Self {
exploration_level: 0,
allow_swap3: true,
granularity: 40,
granularity2: 20,
penalty_tw: p,
penalty_capa: p,
target_ratio: 0.2,
max_it_noimprov: 0,
max_it_total: 0,
nb_it_adapt_penalties: 100,
nb_it_traces: 100,
mu: 2,
mu_start: 5,
lambda: 1,
nb_close: 1,
nb_elite: 1,
},
2 => Self {
exploration_level: 1,
allow_swap3: true,
granularity: 40,
granularity2: 20,
penalty_tw: p,
penalty_capa: p,
target_ratio: 0.2,
max_it_noimprov: 10,
max_it_total: 50,
nb_it_adapt_penalties: 100,
nb_it_traces: 100,
mu: 3,
mu_start: 6,
lambda: 3,
nb_close: 1,
nb_elite: 1,
},
3 => Self {
exploration_level: 2,
allow_swap3: true,
granularity: 40,
granularity2: 20,
penalty_tw: p,
penalty_capa: p,
target_ratio: 0.2,
max_it_noimprov: 100,
max_it_total: 500,
nb_it_adapt_penalties: 20,
nb_it_traces: 20,
mu: 5,
mu_start: 10,
lambda: 5,
nb_close: 2,
nb_elite: 2,
},
4 => Self {
exploration_level: 3,
allow_swap3: false,
granularity: 30,
granularity2: 20,
penalty_tw: p,
penalty_capa: p,
target_ratio: 0.2,
max_it_noimprov: 500,
max_it_total: 5_000,
nb_it_adapt_penalties: 20,
nb_it_traces: 100,
mu: 10,
mu_start: 20,
lambda: 10,
nb_close: 2,
nb_elite: 3,
},
5 => Self {
exploration_level: 4,
allow_swap3: false,
granularity: 30,
granularity2: 20,
penalty_tw: p,
penalty_capa: p,
target_ratio: 0.2,
max_it_noimprov: 5_000,
max_it_total: 50_000,
nb_it_adapt_penalties: 50,
nb_it_traces: 200,
mu: 12,
mu_start: 24,
lambda: 20,
nb_close: 3,
nb_elite: 4,
},
6 => Self {
exploration_level: 5,
allow_swap3: false,
granularity: 30,
granularity2: 20,
penalty_tw: p,
penalty_capa: p,
target_ratio: 0.2,
max_it_noimprov: 10_000,
max_it_total: 200_000,
nb_it_adapt_penalties: 50,
nb_it_traces: 500,
mu: 25,
mu_start: 50,
lambda: 40,
nb_close: 3,
nb_elite: 8,
},
_ => Self::defaults(nb_nodes),
}
}
pub fn defaults(nb_nodes: usize) -> Self {
Self::preset(0, nb_nodes)
}
pub fn initialize(hyperparameters: &Option<Map<String, Value>>, nb_nodes: usize) -> Self {
let mut base_params = Self::defaults(nb_nodes);
if let Some(v) = hyperparameters.as_ref().and_then(|m| m.get("exploration_level")) {
match v {
Value::Number(n) => {
if let Some(u) = n.as_u64() {
base_params = Self::preset(u as usize, nb_nodes);
}
}
Value::String(s) => {
if let Ok(u) = s.parse::<usize>() {
base_params = Self::preset(u, nb_nodes);
}
}
_ => {}
}
}
let mut merged_params = serde_json::to_value(base_params).expect("Config serializable");
if let (Value::Object(ref mut obj), Some(map)) = (&mut merged_params, hyperparameters) {
for (k, v) in map {
if k == "exploration_level" {
continue;
}
obj.insert(k.clone(), v.clone());
}
}
serde_json::from_value(merged_params).unwrap_or_else(|_| Self::defaults(nb_nodes))
}
}

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use super::instance::Instance;
use super::config::Config;
use super::solution::Individual;
use super::gene_pool::{GenePool, Metric};
use super::builder::Builder;
use super::operators::LocalOps;
use super::route_eval::RouteEval;
use rand::rngs::SmallRng;
use rand::Rng;
use rand::seq::SliceRandom;
use tig_challenges::vehicle_routing::*;
use anyhow::Result;
use std::time::Instant;
pub struct Evolution<'a> {
pub data: &'a Instance,
pub params: Config,
pub population: GenePool<'a>,
}
impl<'a> Evolution<'a> {
pub fn new(data: &'a Instance, params: Config) -> Self {
let population = GenePool::new(data);
Self { data, params, population }
}
fn repair_and_maybe_add(&mut self, ls: &mut LocalOps, rng: &mut SmallRng) {
let mut repaired_routes1: Vec<Vec<usize>> = Vec::new();
ls.runls(&mut repaired_routes1, rng, &self.params, true, 100);
let repaired1 = Individual::new_from_routes(self.data, &self.params, repaired_routes1);
if repaired1.load_excess == 0 && repaired1.tw_violation == 0 {
self.population.add(repaired1, &self.params);
}
}
pub fn generate_initial_individual(&mut self, rng: &mut SmallRng, ls: &mut LocalOps, randomize: bool) {
let mut routes: Vec<Vec<usize>> = Builder::build_routes(self.data, rng, randomize);
ls.runls(&mut routes, rng, &self.params, false, 0);
let ind = Individual::new_from_routes(self.data, &self.params, routes);
let is_capa_feasible = ind.load_excess == 0;
let is_tw_feasible = ind.tw_violation == 0;
self.population.add(ind, &self.params);
self.population.record_and_adapt(is_capa_feasible, is_tw_feasible, &mut self.params);
if !is_capa_feasible || !is_tw_feasible {
self.repair_and_maybe_add(ls, rng);
}
}
pub fn generate_crossover_individual(&mut self, rng: &mut SmallRng, ls: &mut LocalOps) {
let p1 = self.population.get_binary_tournament(rng);
let mut p2 = self.population.get_binary_tournament(rng);
while std::ptr::eq(p1, p2) {
p2 = self.population.get_binary_tournament(rng);
}
let t2 = self.extract_giant_tour(&p2.routes);
let extra = if rng.gen_ratio(1, 10) { 1 } else { 0 };
let target_routes = (p1.nb_routes + extra).clamp(self.data.lb_vehicles, self.data.nb_vehicles);
let mut child_tour = self.crossover_rbx(p1, &t2, rng);
self.mutate_tour(&mut child_tour, rng);
let mut child_routes = self.split(&child_tour, target_routes);
ls.runls(&mut child_routes, rng, &self.params, false, 0);
let child = Individual::new_from_routes(self.data, &self.params, child_routes);
let is_capa_feasible = child.load_excess == 0;
let is_tw_feasible = child.tw_violation == 0;
self.population.add(child, &self.params);
self.population.record_and_adapt(is_capa_feasible, is_tw_feasible, &mut self.params);
if !is_capa_feasible || !is_tw_feasible {
self.repair_and_maybe_add(ls, rng);
}
}
pub fn run(
&mut self,
rng: &mut SmallRng,
t0: &Instant,
save_solution: Option<&dyn Fn(&Solution) -> Result<()>>,
) -> Option<(Vec<Vec<usize>>, i32)> {
if let Some(save) = save_solution {
let dummy_routes: Vec<Vec<usize>> = (1..self.data.nb_nodes).map(|i| vec![0, i, 0]).collect();
let _ = save(&Solution { routes: dummy_routes });
}
let mut ls = LocalOps::new(self.data, self.params);
let diversity_boost = if self.data.nb_nodes < 1000 { 3 } else { 1 };
for it in 0..(self.params.mu_start + diversity_boost) {
self.generate_initial_individual(rng, &mut ls, it > 0);
}
let mut best_metric: Metric = self.population.best_metric();
let mut it_noimprov: usize = 0;
let mut it_total: usize = 0;
while it_noimprov < self.params.max_it_noimprov && it_total < self.params.max_it_total {
self.generate_crossover_individual(rng, &mut ls);
if it_total % self.params.nb_it_traces == 0 {
self.population
.print_trace(it_total, it_noimprov, t0.elapsed().as_secs_f64(), &self.params);
}
let cur = self.population.best_metric();
if cur.better_than(best_metric) {
best_metric = cur;
it_noimprov = 0;
if let Some(best) = self.population.best_feasible() {
if let Some(save) = save_solution {
let _ = save(&Solution { routes: best.routes });
}
}
} else {
it_noimprov += 1;
}
it_total += 1;
}
if let Some(best) = self.population.best_feasible() {
let mut best_routes = best.routes.clone();
ls.runls(&mut best_routes, rng, &self.params, false, 0);
let best_after = Individual::new_from_routes(self.data, &self.params, best_routes);
let chosen =
if best_after.tw_violation == 0 && best_after.load_excess == 0 && best_after.distance < best.distance {
best_after
} else {
best
};
if let Some(save) = save_solution {
let _ = save(&Solution { routes: chosen.routes.clone() });
}
Some((chosen.routes, chosen.cost as i32))
} else {
None
}
}
fn mutate_tour(&self, tour: &mut Vec<usize>, rng: &mut SmallRng) {
let n = tour.len();
if n < 4 {
return;
}
if rng.gen_ratio(1, 5) {
let i = rng.gen_range(0..n - 1);
let j = rng.gen_range(i + 1..n);
tour[i..=j].reverse();
}
if rng.gen_ratio(1, 6) {
let i = rng.gen_range(0..n);
let mut j = rng.gen_range(0..n);
if j == i {
j = (j + 1) % n;
}
let node = tour.remove(i);
let pos = if j <= tour.len() { j } else { tour.len() };
tour.insert(pos, node);
}
if n >= 8 && rng.gen_ratio(1, 8) {
let mut cuts = [0usize; 4];
for c in cuts.iter_mut() {
*c = rng.gen_range(1..n);
}
cuts.sort_unstable();
let a = cuts[0];
let b = cuts[1];
let c = cuts[2];
let d = cuts[3];
let mut new_tour = Vec::with_capacity(n);
new_tour.extend_from_slice(&tour[0..a]);
new_tour.extend_from_slice(&tour[c..d]);
new_tour.extend_from_slice(&tour[b..c]);
new_tour.extend_from_slice(&tour[a..b]);
new_tour.extend_from_slice(&tour[d..n]);
*tour = new_tour;
}
}
pub fn split(&self, giant: &Vec<usize>, target_routes: usize) -> Vec<Vec<usize>> {
let n = giant.len();
if n == 0 {
return Vec::new();
}
let k = target_routes.max(1);
let inf = i64::MAX / 4;
let mut dp = vec![vec![inf; n + 1]; k + 1];
let mut pred = vec![vec![0usize; n + 1]; k + 1];
dp[0][0] = 0;
let factor_split: f32 = 1.5;
let cap_limit: i32 = (factor_split * (self.data.max_capacity as f32)) as i32;
let depot = RouteEval::singleton(self.data, 0);
for kk in 1..=k {
for i in (kk - 1)..n {
let base = dp[kk - 1][i];
if base >= inf {
continue;
}
let mut acc = RouteEval::join2(self.data, &depot, &RouteEval::singleton(self.data, giant[i]));
for j in (i + 1)..=n {
let cost = RouteEval::eval2(self.data, &self.params, &acc, &depot);
let cand = base + cost;
if cand < dp[kk][j] {
dp[kk][j] = cand;
pred[kk][j] = i;
}
if acc.load > cap_limit {
break;
}
if j < n {
let next = RouteEval::singleton(self.data, giant[j]);
acc = RouteEval::join2(self.data, &acc, &next);
}
}
}
}
let mut best_k = 1usize;
let mut best_val = dp[1][n];
for kk in 2..=k {
let val = dp[kk][n];
if val < best_val {
best_val = val;
best_k = kk;
}
}
if best_val >= inf {
let mut routes: Vec<Vec<usize>> = Vec::with_capacity(n);
for &id in giant {
routes.push(vec![0, id, 0]);
}
return routes;
}
let mut routes: Vec<Vec<usize>> = Vec::with_capacity(best_k);
let mut j = n;
for kk in (1..=best_k).rev() {
let i = pred[kk][j];
let mut r: Vec<usize> = Vec::with_capacity((j - i) + 2);
r.push(0);
for p in i..j {
r.push(giant[p]);
}
r.push(0);
routes.push(r);
j = i;
}
routes.reverse();
routes
}
pub fn extract_giant_tour(&self, routes: &[Vec<usize>]) -> Vec<usize> {
let (x0, y0) = (self.data.node_positions[0].0 as f64, self.data.node_positions[0].1 as f64);
let mut route_angles: Vec<(f64, usize)> = Vec::new();
for (r_idx, r) in routes.iter().enumerate() {
if r.len() <= 2 {
continue;
}
let mut sum_x = 0.0;
let mut sum_y = 0.0;
let mut cnt = 0usize;
for &id in r.iter().skip(1).take(r.len().saturating_sub(2)) {
sum_x += self.data.node_positions[id].0 as f64;
sum_y += self.data.node_positions[id].1 as f64;
cnt += 1;
}
let bx = sum_x / (cnt as f64);
let by = sum_y / (cnt as f64);
let angle = (by - y0).atan2(bx - x0);
route_angles.push((angle, r_idx));
}
route_angles.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal));
let mut tour = Vec::with_capacity(self.data.nb_nodes - 1);
for &(_, r_idx) in &route_angles {
let r = &routes[r_idx];
for &id in r.iter().skip(1).take(r.len().saturating_sub(2)) {
if id != 0 {
tour.push(id);
}
}
}
tour
}
fn crossover_rbx(&self, p1: &Individual, t2: &Vec<usize>, rng: &mut SmallRng) -> Vec<usize> {
let n = self.data.nb_nodes - 1;
if n == 0 {
return Vec::new();
}
let mut cand: Vec<usize> = Vec::new();
for (idx, r) in p1.routes.iter().enumerate() {
if r.len() > 2 {
cand.push(idx);
}
}
if cand.is_empty() {
return t2.clone();
}
cand.shuffle(rng);
let keep = rng.gen_range(1..=cand.len().min(3));
cand.truncate(keep);
let mut used = vec![false; self.data.nb_nodes];
let mut child: Vec<usize> = Vec::with_capacity(n);
for &ri in &cand {
let r = &p1.routes[ri];
for &id in r.iter().skip(1).take(r.len() - 2) {
if !used[id] {
used[id] = true;
child.push(id);
}
}
}
for &id in t2 {
if !used[id] {
used[id] = true;
child.push(id);
}
}
if child.len() < n {
for id in 1..self.data.nb_nodes {
if !used[id] {
used[id] = true;
child.push(id);
if child.len() == n {
break;
}
}
}
} else if child.len() > n {
child.truncate(n);
}
child
}
}

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use super::instance::Instance;
use super::config::Config;
use super::solution::Individual;
use rand::rngs::SmallRng;
use rand::Rng;
use std::collections::VecDeque;
#[derive(Default)]
pub struct Subpopulation {
pub indivs: Vec<Individual>,
pub prox: Vec<Vec<(f64, usize)>>,
pub biased_fitness: Vec<f64>,
pub order_cost: Vec<usize>,
}
pub struct GenePool<'a> {
pub data: &'a Instance,
pub feasible: Subpopulation,
pub infeasible: Subpopulation,
cap_window: VecDeque<bool>,
tw_window: VecDeque<bool>,
since_last_adapt: usize,
}
impl<'a> GenePool<'a> {
pub fn new(data: &'a Instance) -> Self {
Self {
data,
feasible: Subpopulation::default(),
infeasible: Subpopulation::default(),
cap_window: VecDeque::new(),
tw_window: VecDeque::new(),
since_last_adapt: 0,
}
}
pub fn survivors_selection(sub: &mut Subpopulation, params: &Config) {
while sub.indivs.len() > params.mu {
let idx = Self::worst_index_biased_with_clone_priority(sub);
Self::remove_at_index(sub, idx);
Self::order_cost_rebuild(sub);
Self::update_biased_fitnesses(sub, params);
}
}
pub fn add(&mut self, ind: Individual, params: &Config) {
let is_feasible = ind.load_excess == 0 && ind.tw_violation == 0;
let sub = if is_feasible { &mut self.feasible } else { &mut self.infeasible };
let new_idx = sub.indivs.len();
sub.indivs.push(ind);
Self::prox_add(sub, self.data, new_idx);
Self::order_cost_rebuild(sub);
Self::update_biased_fitnesses(sub, params);
if sub.indivs.len() > params.mu + params.lambda {
Self::survivors_selection(sub, params);
}
}
pub fn record_and_adapt(&mut self, cap_feasible: bool, tw_feasible: bool, params: &mut Config) {
let period = params.nb_it_adapt_penalties;
self.cap_window.push_back(cap_feasible);
self.tw_window.push_back(tw_feasible);
if self.cap_window.len() > period {
self.cap_window.pop_front();
}
if self.tw_window.len() > period {
self.tw_window.pop_front();
}
self.since_last_adapt += 1;
if self.since_last_adapt == period {
let cap_ok = self.cap_window.iter().rev().take(period).filter(|&&b| b).count();
let tw_ok = self.tw_window.iter().rev().take(period).filter(|&&b| b).count();
let frac_cap = (cap_ok as f64) / (period as f64);
let frac_tw = (tw_ok as f64) / (period as f64);
if frac_cap < params.target_ratio {
params.penalty_capa = (((params.penalty_capa as f64) * 1.3).ceil()).clamp(1.0, 10_000.0) as usize;
} else {
params.penalty_capa = (((params.penalty_capa as f64) * 0.7).floor()).clamp(1.0, 10_000.0) as usize;
}
if frac_tw < params.target_ratio {
params.penalty_tw = (((params.penalty_tw as f64) * 1.3).ceil()).clamp(1.0, 10_000.0) as usize;
} else {
params.penalty_tw = (((params.penalty_tw as f64) * 0.7).floor()).clamp(1.0, 10_000.0) as usize;
}
self.since_last_adapt = 0;
self.recompute_costs(params);
}
}
pub fn recompute_costs(&mut self, params: &Config) {
for ind in self.feasible.indivs.iter_mut() {
ind.recompute_cost(params);
}
for ind in self.infeasible.indivs.iter_mut() {
ind.recompute_cost(params);
}
Self::order_cost_rebuild(&mut self.feasible);
Self::order_cost_rebuild(&mut self.infeasible);
Self::update_biased_fitnesses(&mut self.feasible, params);
Self::update_biased_fitnesses(&mut self.infeasible, params);
}
pub fn best_feasible(&self) -> Option<Individual> {
if !self.feasible.indivs.is_empty() {
return Some(self.feasible.indivs[self.feasible.order_cost[0]].clone());
}
None
}
pub fn get_binary_tournament<'b>(&'b self, rng: &mut SmallRng) -> &'b Individual {
let feas_n = self.feasible.indivs.len();
let inf_n = self.infeasible.indivs.len();
let pick = |rng: &mut SmallRng| -> (bool, usize, f64) {
if feas_n > 0 && (inf_n == 0 || rng.gen_ratio(3, 4)) {
let i = rng.gen_range(0..feas_n);
(true, i, self.feasible.biased_fitness[i])
} else {
let i = rng.gen_range(0..inf_n);
(false, i, self.infeasible.biased_fitness[i])
}
};
let (f1, i1, b1) = pick(rng);
let (f2, i2, b2) = pick(rng);
if b1 <= b2 {
if f1 { &self.feasible.indivs[i1] } else { &self.infeasible.indivs[i1] }
} else if f2 {
&self.feasible.indivs[i2]
} else {
&self.infeasible.indivs[i2]
}
}
pub fn best_metric(&self) -> Metric {
if !self.feasible.indivs.is_empty() {
let mut best_d = i32::MAX;
for ind in &self.feasible.indivs {
if ind.distance < best_d {
best_d = ind.distance;
}
}
return Metric {
feasible: true,
distance: best_d,
infeas_sum: 0,
};
}
let mut best_sum = i32::MAX;
let mut best_dist = i32::MAX;
for ind in &self.infeasible.indivs {
let s = ind.load_excess + ind.tw_violation;
if s < best_sum || (s == best_sum && ind.distance < best_dist) {
best_sum = s;
best_dist = ind.distance;
}
}
Metric {
feasible: false,
distance: best_dist,
infeas_sum: best_sum,
}
}
pub fn print_trace(&self, _it_total: usize, _it_no_improve: usize, _elapsed_sec: f64, _params: &Config) {}
fn worst_index_biased_with_clone_priority(sub: &Subpopulation) -> usize {
const CLONE_EPS: f64 = 1e-6;
let mut worst_idx = 0usize;
let mut worst_is_clone = (sub.prox[0][0].0 <= CLONE_EPS) as u8;
let mut worst_fit = sub.biased_fitness[0];
for i in 1..sub.indivs.len() {
let is_clone = (sub.prox[i][0].0 <= CLONE_EPS) as u8;
let bf = sub.biased_fitness[i];
if is_clone > worst_is_clone || (is_clone == worst_is_clone && bf > worst_fit) {
worst_is_clone = is_clone;
worst_fit = bf;
worst_idx = i;
}
}
worst_idx
}
fn prox_add(sub: &mut Subpopulation, data: &Instance, new_idx: usize) {
let m = sub.indivs.len();
sub.prox.push(Vec::with_capacity(m.saturating_sub(1)));
for i in 0..new_idx {
let d = Self::broken_pairs_distance(data, &sub.indivs[new_idx], &sub.indivs[i]);
let vec_i = &mut sub.prox[i];
let pos = vec_i.partition_point(|(dd, _)| *dd <= d);
vec_i.insert(pos, (d, new_idx));
sub.prox[new_idx].push((d, i));
}
sub.prox[new_idx].sort_unstable_by(|a, b| a.0.partial_cmp(&b.0).unwrap());
}
fn remove_at_index(sub: &mut Subpopulation, idx: usize) {
let last = sub.indivs.len() - 1;
sub.indivs.swap_remove(idx);
sub.prox.swap_remove(idx);
sub.biased_fitness.swap_remove(idx);
for list in sub.prox.iter_mut() {
list.retain(|&(_, j)| j != idx);
if last != idx {
for pair in list.iter_mut() {
if pair.1 == last {
pair.1 = idx;
}
}
}
}
}
fn order_cost_rebuild(sub: &mut Subpopulation) {
sub.order_cost.clear();
sub.order_cost.extend(0..sub.indivs.len());
sub.order_cost.sort_unstable_by_key(|&i| sub.indivs[i].cost);
}
fn update_biased_fitnesses(sub: &mut Subpopulation, params: &Config) {
let n = sub.indivs.len();
if n == 0 {
return;
}
sub.biased_fitness.resize(n, 0.0);
if n == 1 {
sub.biased_fitness[0] = 0.0;
return;
}
let nb_close = params.nb_close.min(n - 1);
let mut avg_closest = vec![0.0; n];
for i in 0..n {
let neighbors = &sub.prox[i];
let mut sum = 0.0;
for t in 0..nb_close {
sum += neighbors[t].0;
}
avg_closest[i] = sum / (nb_close as f64);
}
let mut div_pairs: Vec<(f64, usize)> = (0..n).map(|i| (-avg_closest[i], i)).collect();
div_pairs.sort_unstable_by(|a, b| a.0.partial_cmp(&b.0).unwrap());
let denom = (n - 1) as f64;
let mut div_rank = vec![0.0; n];
for (pos, &(_, idx)) in div_pairs.iter().enumerate() {
div_rank[idx] = (pos as f64) / denom;
}
let mut cost_pos = vec![0usize; n];
for (pos, &idx) in sub.order_cost.iter().enumerate() {
cost_pos[idx] = pos;
}
let fit_rank: Vec<f64> = cost_pos.iter().map(|&p| (p as f64) / denom).collect();
let scale = 1.0 - (params.nb_elite as f64) / (n as f64);
for i in 0..n {
if cost_pos[i] < params.nb_elite {
sub.biased_fitness[i] = fit_rank[i];
} else {
sub.biased_fitness[i] = fit_rank[i] + scale * div_rank[i];
}
}
}
fn broken_pairs_distance(data: &Instance, indiv_a: &Individual, indiv_b: &Individual) -> f64 {
let pred_a = &indiv_a.pred;
let succ_a = &indiv_a.succ;
let pred_b = &indiv_b.pred;
let succ_b = &indiv_b.succ;
let n_clients = data.nb_nodes - 1;
let mut differences = 0usize;
for j in 1..=n_clients {
if succ_a[j] != succ_b[j] && succ_a[j] != pred_b[j] {
differences += 1;
}
if pred_a[j] == 0 && pred_b[j] != 0 && succ_b[j] != 0 {
differences += 1;
}
}
(differences as f64) / (n_clients as f64)
}
}
#[derive(Copy, Clone, PartialEq, Eq)]
pub struct Metric {
pub feasible: bool,
pub distance: i32,
pub infeas_sum: i32,
}
impl Metric {
#[inline]
pub fn better_than(self, other: Metric) -> bool {
if self.feasible && !other.feasible {
return true;
}
if !self.feasible && other.feasible {
return false;
}
if self.feasible {
self.distance < other.distance
} else if self.infeas_sum != other.infeas_sum {
self.infeas_sum < other.infeas_sum
} else {
self.distance < other.distance
}
}
}

20
tig-algorithms/src/vehicle_routing/fast_lane_v2/instance.rs Normal file
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pub struct Instance {
pub seed: [u8; 32],
pub nb_nodes: usize,
pub nb_vehicles: usize,
pub lb_vehicles: usize,
pub demands: Vec<i32>,
pub max_capacity: i32,
pub distance_matrix: Vec<Vec<i32>>,
pub node_positions: Vec<(i32, i32)>,
pub service_times: Vec<i32>,
pub start_tw: Vec<i32>,
pub end_tw: Vec<i32>,
}
impl Instance {
#[inline(always)]
pub fn dm(&self, i: usize, j: usize) -> i32 {
unsafe { *self.distance_matrix.get_unchecked(i).get_unchecked(j) }
}
}

54
tig-algorithms/src/vehicle_routing/fast_lane_v2/mod.rs Normal file
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mod instance;
mod config;
mod route_eval;
mod solution;
mod builder;
mod operators;
mod gene_pool;
mod evolution;
mod runner;
pub use runner::Solver;
use anyhow::Result;
use serde_json::{Map, Value};
use tig_challenges::vehicle_routing::*;
#[allow(dead_code)]
pub fn solve_challenge(
challenge: &Challenge,
save_solution: &dyn Fn(&Solution) -> Result<()>,
hyperparameters: &Option<Map<String, Value>>,
) -> Result<()> {
match Solver::solve_challenge_instance(challenge, hyperparameters, Some(save_solution))? {
Some(solution) => {
let _ = save_solution(&solution);
Ok(())
}
None => Ok(()),
}
}
pub fn help() {
println!("Fast Lane v2: Hybrid Genetic Algorithm with Route-Based Crossover");
println!("");
println!("RECOMMENDED SETTINGS:");
println!("");
println!("For best quality: {{\"exploration_level\": 3}}");
println!("For balanced quality: {{\"exploration_level\": 1}}");
println!("For fastest runtime: {{\"exploration_level\": 0}} or null");
println!("");
println!("EXPLORATION LEVELS (0-6):");
println!(" 0: Minimal iterations, fastest (~40s total)");
println!(" 1: More initial diversity, slightly slower");
println!(" 2: Light exploration (50 iterations)");
println!(" 3: Balanced (500 iterations, recommended)");
println!(" 4: Deep search (5,000 iterations)");
println!(" 5: Very deep (50,000 iterations)");
println!(" 6: Maximum quality (200,000 iterations)");
println!("");
println!("KEY ALGORITHMIC IMPROVEMENTS:");
println!(" • Route-Based Crossover (RBX): Preserves good route structures");
println!(" • Or-Opt moves: Advanced local search with 2-3 block relocations");
println!(" • Diversity boosting: Extra randomized initial solutions");
}

902
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use super::instance::Instance;
use super::config::Config;
use super::route_eval::RouteEval;
use rand::rngs::SmallRng;
use rand::Rng;
use rand::seq::SliceRandom;
use std::cmp::{max, min};
#[derive(Clone, Default)]
pub struct Node {
id: usize,
seq0_i: RouteEval,
seqi_n: RouteEval,
seq1: RouteEval,
seq12: RouteEval,
seq21: RouteEval,
seq123: RouteEval,
}
impl Node {
#[inline]
fn new(id: usize) -> Self {
Self { id, ..Default::default() }
}
}
#[derive(Clone, Default)]
pub struct Route {
cost: i64,
distance: i32,
load: i32,
tw: i32,
nodes: Vec<Node>,
}
impl Route {
#[inline]
fn new(ids: &[usize]) -> Self {
Self {
nodes: ids.iter().copied().map(Node::new).collect(),
..Default::default()
}
}
}
pub struct LocalOps<'a> {
pub data: &'a Instance,
pub neighbors_before: Vec<Vec<usize>>,
pub neighbors_capacity_swap: Vec<Vec<usize>>,
pub params: Config,
pub cost: i64,
pub routes: Vec<Route>,
pub node_route: Vec<usize>,
pub node_pos: Vec<usize>,
pub empty_routes: Vec<usize>,
pub when_last_modified: Vec<usize>,
pub when_last_tested: Vec<usize>,
pub nb_moves: usize,
}
impl<'a> LocalOps<'a> {
pub fn new(data: &'a Instance, params: Config) -> Self {
let n = data.nb_nodes;
let cap = n.saturating_sub(2);
let keep = min(params.granularity as usize, cap);
let mut neighbors_before: Vec<Vec<usize>> = vec![Vec::new(); n];
for i in 1..n {
let mut prox: Vec<(i32, usize)> = Vec::with_capacity(cap);
for j in 1..n {
if j == i {
continue;
}
let tji = data.dm(j, i);
let wait = (data.start_tw[i] - tji - data.service_times[j] - data.end_tw[j]).max(0);
let late = (data.start_tw[j] + data.service_times[j] + tji - data.end_tw[i]).max(0);
let proxy10 = 10 * tji + 2 * wait + 10 * late;
prox.push((proxy10, j));
}
prox.sort_by_key(|&(p, _)| p);
neighbors_before[i] = prox[..keep].iter().map(|&(_, j)| j).collect();
}
let mut neighbors_capacity_swap: Vec<Vec<usize>> = vec![Vec::new(); n];
let diff_limit = max(4, data.max_capacity / 20);
for i in 1..n {
let di = data.demands[i];
let mut prox: Vec<(i32, usize)> = Vec::with_capacity(n.saturating_sub(1));
for j in 1..n {
if j == i {
continue;
}
if (data.demands[j] - di).abs() <= diff_limit {
let dij = data.dm(i, j);
prox.push((dij, j));
}
}
prox.sort_by_key(|&(d, _)| d);
let m = prox.len().min(params.granularity2 as usize);
neighbors_capacity_swap[i] = prox[..m].iter().map(|&(_, j)| j).collect();
}
Self {
data,
neighbors_before,
neighbors_capacity_swap,
params,
cost: 0,
routes: Vec::new(),
node_route: Vec::new(),
node_pos: Vec::new(),
empty_routes: Vec::new(),
when_last_modified: Vec::new(),
when_last_tested: vec![0; n],
nb_moves: 0,
}
}
fn load_from_routes(&mut self, routes: &Vec<Vec<usize>>) {
let n = self.data.nb_nodes;
let fleet = self.data.nb_vehicles;
let mut src: Vec<Vec<usize>> = Vec::new();
if routes.len() <= fleet {
src.extend(routes.iter().cloned());
} else {
let keep = fleet.saturating_sub(1);
src.extend(routes.iter().take(keep).cloned());
let mut merged = routes[keep].clone();
merged.pop();
for r in routes.iter().skip(fleet) {
if r.len() > 2 {
merged.extend_from_slice(&r[1..r.len() - 1]);
}
}
merged.push(0);
src.push(merged);
}
while src.len() < fleet {
src.push(vec![0, 0]);
}
let all_routes: Vec<Route> = src.iter().map(|r| Route::new(r)).collect();
self.node_route = vec![0; n];
self.node_pos = vec![0; n];
self.empty_routes.clear();
self.routes = all_routes;
self.when_last_modified = vec![0; self.routes.len()];
self.when_last_tested = vec![0; n];
self.nb_moves = 1;
for rid in 0..self.routes.len() {
self.update_route(rid);
}
self.cost = self.routes.iter().map(|r| r.cost).sum();
}
fn write_back_to_routes(&self, out: &mut Vec<Vec<usize>>) {
out.clear();
out.extend(
self.routes
.iter()
.filter(|r| r.nodes.len() > 2)
.map(|r| r.nodes.iter().map(|n| n.id).collect::<Vec<usize>>()),
);
}
fn update_route(&mut self, rid: usize) {
let data = self.data;
let r = &mut self.routes[rid];
let len = r.nodes.len();
let mut acc_fwd = RouteEval::singleton(data, r.nodes[0].id);
r.nodes[0].seq0_i = acc_fwd;
for pos in 1..len {
let id = r.nodes[pos].id;
acc_fwd = RouteEval::join2(data, &acc_fwd, &RouteEval::singleton(data, id));
r.nodes[pos].seq0_i = acc_fwd;
}
let mut acc_bwd = RouteEval::singleton(data, r.nodes[len - 1].id);
r.nodes[len - 1].seqi_n = acc_bwd;
for pos in (0..len - 1).rev() {
let id = r.nodes[pos].id;
acc_bwd = RouteEval::join2(data, &RouteEval::singleton(data, id), &acc_bwd);
r.nodes[pos].seqi_n = acc_bwd;
}
for pos in 0..len {
let id = r.nodes[pos].id;
r.nodes[pos].seq1 = RouteEval::singleton(data, id);
if pos + 1 < len {
let id_next = r.nodes[pos + 1].id;
r.nodes[pos].seq12 =
RouteEval::join2(data, &RouteEval::singleton(data, id), &RouteEval::singleton(data, id_next));
r.nodes[pos].seq21 =
RouteEval::join2(data, &RouteEval::singleton(data, id_next), &RouteEval::singleton(data, id));
if pos + 2 < len {
let id_next2 = r.nodes[pos + 2].id;
r.nodes[pos].seq123 = RouteEval::join2(data, &r.nodes[pos].seq12, &RouteEval::singleton(data, id_next2));
}
}
}
let end = r.nodes[len - 1].seq0_i;
r.load = end.load;
r.tw = end.tw;
r.distance = end.distance;
r.cost = end.eval(data, &self.params);
for (pos, node) in self.routes[rid].nodes.iter().enumerate() {
self.node_route[node.id] = rid;
self.node_pos[node.id] = pos;
}
let is_empty = self.routes[rid].nodes.len() == 2;
let pos = self.empty_routes.iter().position(|&eid| eid == rid);
match (is_empty, pos) {
(true, None) => self.empty_routes.push(rid),
(false, Some(i)) => {
self.empty_routes.swap_remove(i);
}
_ => {}
}
self.when_last_modified[rid] = self.nb_moves;
}
pub fn run_intra_route_relocate(&mut self, r1: usize, pos1: usize) -> bool {
let route = &self.routes[r1];
let len = route.nodes.len();
if len < pos1 + 4 {
return false;
}
let mut left_excl: Vec<RouteEval> = vec![RouteEval::default(); len];
let mut acc_left = route.nodes[0].seq0_i;
for p in 1..len {
left_excl[p] = acc_left;
if p != pos1 {
acc_left = RouteEval::join2(self.data, &acc_left, &route.nodes[p].seq1);
}
}
let mut right_excl: Vec<RouteEval> = vec![RouteEval::default(); len];
let mut acc_right = route.nodes[len - 1].seq1;
right_excl[len - 1] = acc_right;
for p in (1..len - 1).rev() {
if p != pos1 {
acc_right = RouteEval::join2(self.data, &route.nodes[p].seq1, &acc_right);
}
right_excl[p] = acc_right;
}
let old_cost = route.cost;
let mut best_cost = old_cost;
let mut best_pos: Option<usize> = None;
for t in 1..len {
if t == pos1 || t == pos1 + 1 {
continue;
}
let new_cost = RouteEval::eval3(self.data, &self.params, &left_excl[t], &route.nodes[pos1].seq1, &right_excl[t]);
if new_cost < best_cost {
best_cost = new_cost;
best_pos = Some(t);
}
}
if let Some(mypos) = best_pos {
let insert_pos = if mypos > pos1 { mypos - 1 } else { mypos };
let elem = self.routes[r1].nodes.remove(pos1);
self.routes[r1].nodes.insert(insert_pos, elem);
self.nb_moves += 1;
self.update_route(r1);
self.cost += self.routes[r1].cost - old_cost;
true
} else {
false
}
}
pub fn run_intra_route_swap_right(&mut self, r1: usize, pos1: usize) -> bool {
let route = &self.routes[r1];
let len = route.nodes.len();
if len < pos1 + 4 {
return false;
}
let old_cost = route.cost;
let mut best_cost = old_cost;
let mut best_pos: Option<usize> = None;
let mut acc_mid = route.nodes[pos1 + 1].seq1;
for pos2 in (pos1 + 2)..(len - 1) {
let new_cost = RouteEval::eval_n(
self.data,
&self.params,
&[
route.nodes[pos1 - 1].seq0_i,
route.nodes[pos2].seq1,
acc_mid,
route.nodes[pos1].seq1,
route.nodes[pos2 + 1].seqi_n,
],
);
if new_cost < best_cost {
best_cost = new_cost;
best_pos = Some(pos2);
}
acc_mid = RouteEval::join2(self.data, &acc_mid, &route.nodes[pos2].seq1);
}
if let Some(mypos) = best_pos {
self.routes[r1].nodes.swap(pos1, mypos);
self.nb_moves += 1;
self.update_route(r1);
self.cost += self.routes[r1].cost - old_cost;
true
} else {
false
}
}
pub fn run_2optstar(&mut self, r1: usize, pos1: usize, r2: usize, pos2: usize) -> bool {
let route1 = &self.routes[r1];
let route2 = &self.routes[r2];
let new1 = RouteEval::eval2(self.data, &self.params, &route1.nodes[pos1 - 1].seq0_i, &route2.nodes[pos2].seqi_n);
let new2 = RouteEval::eval2(self.data, &self.params, &route2.nodes[pos2 - 1].seq0_i, &route1.nodes[pos1].seqi_n);
let old_cost = route1.cost + route2.cost;
let new_cost = new1 + new2;
if new_cost < old_cost {
let mut suffix1 = self.routes[r1].nodes.split_off(pos1);
let mut suffix2 = self.routes[r2].nodes.split_off(pos2);
self.routes[r1].nodes.append(&mut suffix2);
self.routes[r2].nodes.append(&mut suffix1);
self.nb_moves += 1;
self.update_route(r1);
self.update_route(r2);
self.cost += new_cost - old_cost;
true
} else {
false
}
}
pub fn run_2opt(&mut self, r1: usize, pos1: usize) -> bool {
let route = &self.routes[r1];
let len = route.nodes.len();
if len < pos1 + 3 {
return false;
}
let old_cost = route.cost;
let mut best_cost = old_cost;
let mut best_pos: Option<usize> = None;
let mut mid_rev = route.nodes[pos1].seq21;
for pos2 in (pos1 + 1)..(len - 1) {
let new_cost = RouteEval::eval3(
self.data,
&self.params,
&route.nodes[pos1 - 1].seq0_i,
&mid_rev,
&route.nodes[pos2 + 1].seqi_n,
);
if new_cost < best_cost {
best_cost = new_cost;
best_pos = Some(pos2);
}
if pos2 + 1 < len - 1 {
mid_rev = RouteEval::join2(self.data, &route.nodes[pos2 + 1].seq1, &mid_rev);
}
}
if let Some(mypos) = best_pos {
self.routes[r1].nodes[pos1..=mypos].reverse();
self.nb_moves += 1;
self.update_route(r1);
self.cost += self.routes[r1].cost - old_cost;
true
} else {
false
}
}
pub fn run_intra_route_oropt(&mut self, r1: usize, pos1: usize, l: usize) -> bool {
if l < 2 || l > 3 {
return false;
}
let old_cost = self.routes[r1].cost;
let applied = {
let route = &self.routes[r1];
let len = route.nodes.len();
if pos1 == 0 || pos1 >= len - 1 {
return false;
}
if pos1 + l >= len {
return false;
}
if l == 3 && pos1 + 2 >= len {
return false;
}
let block_seq = if l == 2 { route.nodes[pos1].seq12 } else { route.nodes[pos1].seq123 };
let mut best_cost = old_cost;
let mut best_dir = 0i32;
let mut best_t = 0usize;
let suffix_start = pos1 + l;
let suffix_fixed = route.nodes[suffix_start].seqi_n;
if pos1 > 1 {
let mut mid_seq = route.nodes[pos1 - 1].seq1;
for t in (1..pos1).rev() {
let prefix_seq = route.nodes[t - 1].seq0_i;
let cand = RouteEval::eval_n(self.data, &self.params, &[prefix_seq, block_seq, mid_seq, suffix_fixed]);
if cand < best_cost {
best_cost = cand;
best_dir = -1;
best_t = t;
}
if t > 1 {
mid_seq = RouteEval::join2(self.data, &route.nodes[t - 1].seq1, &mid_seq);
}
}
}
if pos1 + l < len - 1 {
let prefix_seq = route.nodes[pos1 - 1].seq0_i;
let mut mid_seq = route.nodes[pos1 + l].seq1;
for t in (pos1 + l + 1)..len {
let suffix_seq = route.nodes[t].seqi_n;
let cand = RouteEval::eval_n(self.data, &self.params, &[prefix_seq, mid_seq, block_seq, suffix_seq]);
if cand < best_cost {
best_cost = cand;
best_dir = 1;
best_t = t;
}
if t < len - 1 {
mid_seq = RouteEval::join2(self.data, &mid_seq, &route.nodes[t].seq1);
}
}
}
if best_dir == 0 {
return false;
}
let insert_pos = if best_dir > 0 { best_t - l } else { best_t };
let mut blk: Vec<Node> = Vec::with_capacity(l);
for _ in 0..l {
blk.push(self.routes[r1].nodes.remove(pos1));
}
for (k, node) in blk.into_iter().enumerate() {
self.routes[r1].nodes.insert(insert_pos + k, node);
}
true
};
if !applied {
return false;
}
self.nb_moves += 1;
self.update_route(r1);
self.cost += self.routes[r1].cost - old_cost;
true
}
pub fn run_inter_route(&mut self, r1: usize, pos1: usize, r2: usize, pos2: usize) -> bool {
let data = self.data;
let ru = &self.routes[r1];
let rv = &self.routes[r2];
let u = &ru.nodes[pos1];
let v = &rv.nodes[pos2];
let u_pred = &ru.nodes[pos1 - 1];
let v_pred = &rv.nodes[pos2 - 1];
let x = &ru.nodes[pos1 + 1];
let old_total = ru.cost + rv.cost;
let mut best_i = 0usize;
let mut best_j = 0usize;
let mut best_cost = old_total;
let mut update_best = |i: usize, j: usize, cand: i64| {
if cand < best_cost {
best_cost = cand;
best_i = i;
best_j = j;
}
};
let result10 = RouteEval::eval2(data, &self.params, &u_pred.seq0_i, &x.seqi_n)
+ RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq1, &v.seqi_n);
update_best(1, 0, result10);
if v.id != 0 {
let result11 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq1, &x.seqi_n)
+ RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq1, &rv.nodes[pos2 + 1].seqi_n);
update_best(1, 1, result11);
}
if x.id != 0 {
let x_next = &ru.nodes[pos1 + 2];
let mut result20 = RouteEval::eval2(data, &self.params, &u_pred.seq0_i, &x_next.seqi_n);
let mut result30 = result20;
result20 += RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq12, &v.seqi_n);
result30 += RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq21, &v.seqi_n);
update_best(2, 0, result20);
update_best(3, 0, result30);
if v.id != 0 {
let y = &rv.nodes[pos2 + 1];
let mut result21 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq1, &x_next.seqi_n);
let mut result31 = result21;
result21 += RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq12, &y.seqi_n);
result31 += RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq21, &y.seqi_n);
update_best(2, 1, result21);
update_best(3, 1, result31);
if y.id != 0 {
let mut result22 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq12, &x_next.seqi_n);
let mut result23 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq21, &x_next.seqi_n);
let mut result32 = result22;
let mut result33 = result23;
let y_next = &rv.nodes[pos2 + 2];
let tmp = RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq12, &y_next.seqi_n);
let tmp2 = RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq21, &y_next.seqi_n);
result22 += tmp;
result23 += tmp;
result32 += tmp2;
result33 += tmp2;
update_best(2, 2, result22);
update_best(3, 2, result32);
update_best(2, 3, result23);
update_best(3, 3, result33);
}
}
if x_next.id != 0 && self.params.allow_swap3 {
let x2_next = &ru.nodes[pos1 + 3];
let result40 = RouteEval::eval2(data, &self.params, &u_pred.seq0_i, &x2_next.seqi_n)
+ RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq123, &v.seqi_n);
update_best(4, 0, result40);
if v.id != 0 {
let y = &rv.nodes[pos2 + 1];
let result41 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq1, &x2_next.seqi_n)
+ RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq123, &y.seqi_n);
update_best(4, 1, result41);
if y.id != 0 {
let y_next = &rv.nodes[pos2 + 2];
let result42 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq12, &x2_next.seqi_n)
+ RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq123, &y_next.seqi_n);
let result43 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq21, &x2_next.seqi_n)
+ RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq123, &y_next.seqi_n);
update_best(4, 2, result42);
update_best(4, 3, result43);
if y_next.id != 0 {
let y2_next = &rv.nodes[pos2 + 3];
let result44 = RouteEval::eval3(data, &self.params, &u_pred.seq0_i, &v.seq123, &x2_next.seqi_n)
+ RouteEval::eval3(data, &self.params, &v_pred.seq0_i, &u.seq123, &y2_next.seqi_n);
update_best(4, 4, result44);
}
}
}
}
}
if best_i == 0 && best_j == 0 {
return false;
}
let mut take_block = |route_idx: usize, pos: usize, kind: usize| -> Vec<Node> {
let nodes = &mut self.routes[route_idx].nodes;
match kind {
0 => vec![],
1 => {
let n1 = nodes.remove(pos);
vec![n1]
}
2 => {
let n1 = nodes.remove(pos);
let n2 = nodes.remove(pos);
vec![n1, n2]
}
3 => {
let n1 = nodes.remove(pos);
let n2 = nodes.remove(pos);
vec![n2, n1]
}
4 => {
let n1 = nodes.remove(pos);
let n2 = nodes.remove(pos);
let n3 = nodes.remove(pos);
vec![n1, n2, n3]
}
_ => vec![],
}
};
let blk_from_r1 = take_block(r1, pos1, best_i);
let blk_from_r2 = take_block(r2, pos2, best_j);
let nodes1 = &mut self.routes[r1].nodes;
for (k, node) in blk_from_r2.into_iter().enumerate() {
nodes1.insert(pos1 + k, node);
}
let nodes2 = &mut self.routes[r2].nodes;
for (k, node) in blk_from_r1.into_iter().enumerate() {
nodes2.insert(pos2 + k, node);
}
self.nb_moves += 1;
self.update_route(r1);
self.update_route(r2);
let new_total = self.routes[r1].cost + self.routes[r2].cost;
self.cost += new_total - old_total;
true
}
pub fn run_swapstar(&mut self, r1: usize, pos1: usize, r2: usize, pos2: usize) -> bool {
let route1_len = self.routes[r1].nodes.len();
let route2_len = self.routes[r2].nodes.len();
let u = self.routes[r1].nodes[pos1].id;
let v = self.routes[r2].nodes[pos2].id;
let (pu, nu) = (self.routes[r1].nodes[pos1 - 1].id, self.routes[r1].nodes[pos1 + 1].id);
let (pv, nv) = (self.routes[r2].nodes[pos2 - 1].id, self.routes[r2].nodes[pos2 + 1].id);
let dr1 = self.data.dm(pu, nu) - self.data.dm(pu, u) - self.data.dm(u, nu);
let dr2 = self.data.dm(pv, nv) - self.data.dm(pv, v) - self.data.dm(v, nv);
let delta_demand = self.data.demands[v] - self.data.demands[u];
let new_load1 = self.routes[r1].load + delta_demand;
let new_load2 = self.routes[r2].load - delta_demand;
let new_pen1 = ((new_load1 - self.data.max_capacity).max(0) as i64) * self.params.penalty_capa as i64;
let new_pen2 = ((new_load2 - self.data.max_capacity).max(0) as i64) * self.params.penalty_capa as i64;
let cost_lb_r1_after_removal = (self.routes[r1].distance + dr1) as i64 + new_pen1;
let cost_lb_r2_after_removal = (self.routes[r2].distance + dr2) as i64 + new_pen2;
let mut lb_new_total = cost_lb_r1_after_removal + cost_lb_r2_after_removal;
let old_total = self.routes[r1].cost + self.routes[r2].cost;
if lb_new_total > old_total {
return false;
}
let hole_v = self.data.dm(pu, v) + self.data.dm(v, nu) - self.data.dm(pu, nu);
let mut best_ins_v = hole_v;
for t in 1..route1_len {
let a_id = self.routes[r1].nodes[t - 1].id;
let b_id = self.routes[r1].nodes[t].id;
if a_id == u || b_id == u {
continue;
}
let delta = self.data.dm(a_id, v) + self.data.dm(v, b_id) - self.data.dm(a_id, b_id);
if delta < best_ins_v {
best_ins_v = delta;
}
}
let hole_u = self.data.dm(pv, u) + self.data.dm(u, nv) - self.data.dm(pv, nv);
let mut best_ins_u = hole_u;
for t in 1..route2_len {
let a_id = self.routes[r2].nodes[t - 1].id;
let b_id = self.routes[r2].nodes[t].id;
if a_id == v || b_id == v {
continue;
}
let delta = self.data.dm(a_id, u) + self.data.dm(u, b_id) - self.data.dm(a_id, b_id);
if delta < best_ins_u {
best_ins_u = delta;
}
}
lb_new_total += (best_ins_v + best_ins_u) as i64;
if lb_new_total > old_total {
return false;
}
let mut left_excl1: Vec<RouteEval> = vec![RouteEval::default(); route1_len];
let mut right_excl1: Vec<RouteEval> = vec![RouteEval::default(); route1_len];
{
let r = &self.routes[r1];
let mut acc_left = r.nodes[0].seq0_i;
for p in 1..route1_len {
left_excl1[p] = acc_left;
if p != pos1 {
acc_left = RouteEval::join2(self.data, &acc_left, &r.nodes[p].seq1);
}
}
let mut acc_right = r.nodes[route1_len - 1].seq1;
right_excl1[route1_len - 1] = acc_right;
for p in (1..route1_len - 1).rev() {
if p != pos1 {
acc_right = RouteEval::join2(self.data, &r.nodes[p].seq1, &acc_right);
}
right_excl1[p] = acc_right;
}
}
let mut left_excl2: Vec<RouteEval> = vec![RouteEval::default(); route2_len];
let mut right_excl2: Vec<RouteEval> = vec![RouteEval::default(); route2_len];
{
let r = &self.routes[r2];
let mut acc_left = r.nodes[0].seq0_i;
for p in 1..route2_len {
left_excl2[p] = acc_left;
if p != pos2 {
acc_left = RouteEval::join2(self.data, &acc_left, &r.nodes[p].seq1);
}
}
let mut acc_right = r.nodes[route2_len - 1].seq1;
right_excl2[route2_len - 1] = acc_right;
for p in (1..route2_len - 1).rev() {
if p != pos2 {
acc_right = RouteEval::join2(self.data, &r.nodes[p].seq1, &acc_right);
}
right_excl2[p] = acc_right;
}
}
let v_seq1 = self.routes[r2].nodes[pos2].seq1;
let mut best_cost1 = i64::MAX / 4;
let mut best_t1: usize = 1;
for t in 1..route1_len {
let cand = RouteEval::eval3(self.data, &self.params, &left_excl1[t], &v_seq1, &right_excl1[t]);
if cand < best_cost1 {
best_cost1 = cand;
best_t1 = t;
}
}
let u_seq1 = self.routes[r1].nodes[pos1].seq1;
let mut best_cost2 = i64::MAX / 4;
let mut best_t2: usize = 1;
for t in 1..route2_len {
let cand = RouteEval::eval3(self.data, &self.params, &left_excl2[t], &u_seq1, &right_excl2[t]);
if cand < best_cost2 {
best_cost2 = cand;
best_t2 = t;
}
}
if best_cost1 + best_cost2 >= old_total {
return false;
}
let node_u = self.routes[r1].nodes[pos1].clone();
let node_v = self.routes[r2].nodes[pos2].clone();
self.routes[r1].nodes.remove(pos1);
self.routes[r2].nodes.remove(pos2);
let ins1 = if best_t1 > pos1 { best_t1 - 1 } else { best_t1 };
let ins2 = if best_t2 > pos2 { best_t2 - 1 } else { best_t2 };
self.routes[r1].nodes.insert(ins1, node_v);
self.routes[r2].nodes.insert(ins2, node_u);
self.nb_moves += 1;
self.update_route(r1);
self.update_route(r2);
let new_total = self.routes[r1].cost + self.routes[r2].cost;
self.cost += new_total - old_total;
true
}
pub fn runls(
&mut self,
routes: &mut Vec<Vec<usize>>,
rng: &mut SmallRng,
params: &Config,
is_repair: bool,
factor: usize,
) {
self.params = *params;
if !is_repair {
self.load_from_routes(routes);
} else {
self.params.penalty_tw = (factor * self.params.penalty_tw).min(10_000);
self.params.penalty_capa = (factor * self.params.penalty_capa).min(10_000);
self.nb_moves += 1;
for rid in 0..self.routes.len() {
let r = &self.routes[rid];
if r.load > self.data.max_capacity || r.tw > 0 {
self.update_route(rid);
}
}
}
let mut improved = true;
let mut loop_id = 0;
let mut c1_order: Vec<usize> = (1..self.data.nb_nodes).collect();
while improved {
improved = false;
loop_id += 1;
c1_order.shuffle(rng);
for &c1 in &c1_order {
let last_tested = self.when_last_tested[c1];
self.when_last_tested[c1] = self.nb_moves;
let r1 = self.node_route[c1];
let pos1 = self.node_pos[c1];
let neigh_len = self.neighbors_before[c1].len();
let start = rng.gen_range(0..neigh_len);
for off in 0..neigh_len {
let c2 = self.neighbors_before[c1][(start + off) % neigh_len];
let r2 = self.node_route[c2];
let pos2 = self.node_pos[c2];
if r1 == r2 {
continue;
}
if self.when_last_modified[r1].max(self.when_last_modified[r2]) <= last_tested {
continue;
}
if self.run_inter_route(r1, pos1, r2, pos2 + 1) {
improved = true;
break;
}
if pos1 == 1 && self.run_inter_route(r2, pos2, r1, pos1) {
improved = true;
break;
}
if self.run_2optstar(r1, pos1, r2, pos2 + 1) {
improved = true;
break;
}
}
let r1 = self.node_route[c1];
let pos1 = self.node_pos[c1];
let swap_len = self.neighbors_capacity_swap[c1].len();
if swap_len > 0 {
let start_s = rng.gen_range(0..swap_len);
for off in 0..swap_len {
let c2 = self.neighbors_capacity_swap[c1][(start_s + off) % swap_len];
let r2 = self.node_route[c2];
if r1 == r2 {
continue;
}
if c1 < c2 || self.when_last_modified[r1].max(self.when_last_modified[r2]) <= last_tested {
continue;
}
let pos2 = self.node_pos[c2];
if self.run_swapstar(r1, pos1, r2, pos2) {
improved = true;
break;
}
}
}
let r1 = self.node_route[c1];
let pos1 = self.node_pos[c1];
if loop_id > 1 && (loop_id == 2 || self.when_last_modified[r1] > last_tested) {
if let Some(&r2) = self.empty_routes.first() {
let pos2 = 1;
if self.run_2optstar(r1, pos1, r2, pos2) {
improved = true;
break;
}
if self.run_inter_route(r1, pos1, r2, pos2) {
improved = true;
break;
}
}
}
let r1 = self.node_route[c1];
if self.when_last_modified[r1] > last_tested {
improved |= self.run_intra_route_relocate(self.node_route[c1], self.node_pos[c1]);
improved |= self.run_intra_route_swap_right(self.node_route[c1], self.node_pos[c1]);
improved |= self.run_2opt(self.node_route[c1], self.node_pos[c1]);
improved |= self.run_intra_route_oropt(self.node_route[c1], self.node_pos[c1], 2);
if self.params.allow_swap3 {
improved |= self.run_intra_route_oropt(self.node_route[c1], self.node_pos[c1], 3);
}
}
}
}
self.write_back_to_routes(routes);
}
}

126
tig-algorithms/src/vehicle_routing/fast_lane_v2/route_eval.rs Normal file
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@ -0,0 +1,126 @@
use super::instance::Instance;
use super::config::Config;
use std::cmp::{max, min};
#[derive(Copy, Clone, Default)]
pub struct RouteEval {
pub tau_minus: i32,
pub tau_plus: i32,
pub tmin: i32,
pub tw: i32,
pub total_service_duration: i32,
pub load: i32,
pub distance: i32,
pub first_node: usize,
pub last_node: usize,
}
impl RouteEval {
#[inline(always)]
pub fn initialize(&mut self, data: &Instance, node: usize) {
let st = data.start_tw[node];
let et = data.end_tw[node];
let svc = data.service_times[node];
let ld = data.demands[node];
self.tau_minus = st;
self.tau_plus = et;
self.tmin = svc;
self.tw = 0;
self.total_service_duration = svc;
self.load = ld;
self.distance = 0;
self.first_node = node;
self.last_node = node;
}
#[inline(always)]
pub fn join2(data: &Instance, s1: &RouteEval, s2: &RouteEval) -> RouteEval {
let travel = data.dm(s1.last_node, s2.first_node);
let distance = s1.distance + s2.distance + travel;
let temp = travel + s1.tmin - s1.tw;
let wtij = max(s2.tau_minus - temp - s1.tau_plus, 0);
let twij = max(temp + s1.tau_minus - s2.tau_plus, 0);
let tw = s1.tw + s2.tw + twij;
let tmin = temp + s1.tw + s2.tmin + wtij;
let tau_minus = max(s2.tau_minus - temp - wtij, s1.tau_minus);
let tau_plus = min(s2.tau_plus - temp + twij, s1.tau_plus);
let load = s1.load + s2.load;
RouteEval {
tau_minus,
tau_plus,
tmin,
tw,
total_service_duration: s1.total_service_duration + s2.total_service_duration,
load,
distance,
first_node: s1.first_node,
last_node: s2.last_node,
}
}
#[inline(always)]
pub fn singleton(data: &Instance, node: usize) -> RouteEval {
let mut s = RouteEval::default();
s.initialize(data, node);
s
}
#[inline(always)]
pub fn eval(&self, data: &Instance, params: &Config) -> i64 {
let ptw = params.penalty_tw as i64;
let pcap = params.penalty_capa as i64;
let load_excess = (self.load - data.max_capacity).max(0) as i64;
(self.distance as i64) + load_excess * pcap + (self.tw as i64) * ptw
}
#[inline(always)]
pub fn eval2(data: &Instance, params: &Config, s1: &RouteEval, s2: &RouteEval) -> i64 {
let ptw = params.penalty_tw as i64;
let pcap = params.penalty_capa as i64;
let travel = data.dm(s1.last_node, s2.first_node);
let distance = s1.distance + s2.distance + travel;
let temp = s1.tmin - s1.tw + travel;
let tw_viol = s1.tw + s2.tw + max(s1.tau_minus - s2.tau_plus + temp, 0);
let load = s1.load + s2.load;
let load_excess = (load - data.max_capacity).max(0) as i64;
(distance as i64) + load_excess * pcap + (tw_viol as i64) * ptw
}
#[inline(always)]
pub fn eval3(data: &Instance, params: &Config, s1: &RouteEval, s2: &RouteEval, s3: &RouteEval) -> i64 {
let ptw = params.penalty_tw as i64;
let pcap = params.penalty_capa as i64;
let travel12 = data.dm(s1.last_node, s2.first_node);
let distance12 = s1.distance + s2.distance + travel12;
let temp = travel12 + s1.tmin - s1.tw;
let wtij = max(s2.tau_minus - temp - s1.tau_plus, 0);
let twij = max(temp + s1.tau_minus - s2.tau_plus, 0);
let tw_viol12 = s1.tw + s2.tw + twij;
let tmin12 = temp + s1.tw + s2.tmin + wtij;
let tau_m12 = max(s2.tau_minus - temp - wtij, s1.tau_minus);
let travel23 = data.dm(s2.last_node, s3.first_node);
let distance = distance12 + s3.distance + travel23;
let temp2 = travel23 + tmin12 - tw_viol12;
let tw_viol = tw_viol12 + s3.tw + max(tau_m12 - s3.tau_plus + temp2, 0);
let load = s1.load + s2.load + s3.load;
let load_excess = (load - data.max_capacity).max(0) as i64;
(distance as i64) + load_excess * pcap + (tw_viol as i64) * ptw
}
#[inline(always)]
pub fn eval_n(data: &Instance, params: &Config, chain: &[RouteEval]) -> i64 {
let mut agg = chain[0];
for s in &chain[1..chain.len() - 1] {
agg = RouteEval::join2(data, &agg, s);
}
let last = &chain[chain.len() - 1];
RouteEval::eval2(data, params, &agg, last)
}
}

70
tig-algorithms/src/vehicle_routing/fast_lane_v2/runner.rs Normal file
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@ -0,0 +1,70 @@
use super::instance::Instance;
use super::config::Config;
use super::evolution::Evolution;
use anyhow::Result;
use tig_challenges::vehicle_routing::*;
use serde_json::{Map, Value};
use rand::{rngs::SmallRng, SeedableRng};
use std::time::Instant;
pub struct TigLoader;
impl TigLoader {
pub fn load(challenge: &Challenge) -> Instance {
let nb_nodes = challenge.num_nodes;
let nb_vehicles = challenge.fleet_size;
let mut service_times = vec![challenge.service_time; nb_nodes];
service_times[0] = 0;
let total_demand: f64 = challenge.demands.iter().map(|&d| d as f64).sum();
let ratio = total_demand / challenge.max_capacity as f64;
let lb_vehicles = ratio.ceil() as usize;
Instance {
seed: challenge.seed,
nb_nodes,
nb_vehicles,
lb_vehicles,
demands: challenge.demands.clone(),
node_positions: challenge.node_positions.clone(),
max_capacity: challenge.max_capacity,
distance_matrix: challenge.distance_matrix.clone(),
service_times,
start_tw: challenge.ready_times.clone(),
end_tw: challenge.due_times.clone(),
}
}
}
pub struct Solver;
impl Solver {
fn solve(
data: Instance,
params: Config,
t0: &Instant,
save_solution: Option<&dyn Fn(&Solution) -> Result<()>>,
) -> Result<Option<(Solution, i32, usize)>> {
let mut rng = SmallRng::from_seed(data.seed);
let mut ga = Evolution::new(&data, params);
Ok(ga.run(&mut rng, t0, save_solution).map(|(routes, cost)| {
(Solution { routes: routes.clone() }, cost, routes.len())
}))
}
pub fn solve_challenge_instance(
challenge: &Challenge,
hyperparameters: &Option<Map<String, Value>>,
save_solution: Option<&dyn Fn(&Solution) -> Result<()>>,
) -> Result<Option<Solution>> {
let t0 = Instant::now();
let data = TigLoader::load(challenge);
let params = Config::initialize(hyperparameters, data.nb_nodes);
match Self::solve(data, params, &t0, save_solution) {
Ok(Some((solution, _cost, _routes))) => Ok(Some(solution)),
Ok(None) => Ok(None),
Err(_) => Ok(None),
}
}
}

88
tig-algorithms/src/vehicle_routing/fast_lane_v2/solution.rs Normal file
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@ -0,0 +1,88 @@
use super::instance::Instance;
use super::config::Config;
use super::route_eval::RouteEval;
#[derive(Clone)]
pub struct Individual {
pub routes: Vec<Vec<usize>>,
pub nb_routes: usize,
pub distance: i32,
pub tw_violation: i32,
pub load_excess: i32,
pub cost: i64,
pub pred: Vec<usize>,
pub succ: Vec<usize>,
}
impl Individual {
pub fn new_from_routes(data: &Instance, params: &Config, routes: Vec<Vec<usize>>) -> Self {
let (distance, tw_violation, load_excess) = Self::evaluate_routes(data, &routes);
let cost = Self::compute_penalized_cost(distance, tw_violation, load_excess, params);
let (pred, succ, nb_routes) = Self::build_pred_succ_and_count(data, &routes);
Self {
routes,
nb_routes,
distance,
tw_violation,
load_excess,
cost,
pred,
succ,
}
}
pub fn evaluate_routes(data: &Instance, routes: &Vec<Vec<usize>>) -> (i32, i32, i32) {
let mut dist: i32 = 0;
let mut tw: i32 = 0;
let mut loadx: i32 = 0;
for r in routes {
if r.is_empty() {
continue;
}
let mut acc = RouteEval::singleton(data, r[0]);
for idx in 1..r.len() {
let next = RouteEval::singleton(data, r[idx]);
acc = RouteEval::join2(data, &acc, &next);
}
dist += acc.distance;
tw += acc.tw;
let ex = (acc.load - data.max_capacity).max(0);
loadx += ex;
}
(dist, tw, loadx)
}
#[inline]
pub fn compute_penalized_cost(distance: i32, tw_violation: i32, load_excess: i32, params: &Config) -> i64 {
(distance as i64)
+ (params.penalty_tw as i64) * (tw_violation as i64)
+ (params.penalty_capa as i64) * (load_excess as i64)
}
#[inline]
pub fn recompute_cost(&mut self, params: &Config) {
self.cost = Self::compute_penalized_cost(self.distance, self.tw_violation, self.load_excess, params);
}
fn build_pred_succ_and_count(data: &Instance, routes: &Vec<Vec<usize>>) -> (Vec<usize>, Vec<usize>, usize) {
let n_all = data.nb_nodes;
let mut pred = vec![0usize; n_all];
let mut succ = vec![0usize; n_all];
let mut nb_routes: usize = 0;
for r in routes {
if r.len() > 2 {
nb_routes += 1;
}
if r.len() < 2 {
continue;
}
for p in 1..r.len() - 1 {
let id = r[p];
pred[id] = r[p - 1];
succ[id] = r[p + 1];
}
}
(pred, succ, nb_routes)
}
}

3
tig-algorithms/src/vehicle_routing/mod.rs
View File

@ -209,7 +209,8 @@ pub use fast_lane as c002_a092;
// c002_a094
// c002_a095
pub mod fast_lane_v2;
pub use fast_lane_v2 as c002_a095;
// c002_a096