mirror of
https://github.com/L-yang-yang/cugenopt.git
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321 lines
12 KiB
Text
321 lines
12 KiB
Text
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/**
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* E8v2: P2 约束导向 + 分层搜索 — 大规模 & 紧约束实验
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*
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* 设计思路：
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* - 用更大实例 + 更短时间，确保搜索无法完全收敛
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* - VRPTW-20: 20 客户 4 车，紧时间窗 + 容量约束
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* - PrioVRP-50: 50 客户 8 车（随机坐标），优先级偏序约束
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* - 时间预算：1s, 3s（短时间放大策略差异）
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*
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* 对比：baseline / constraint / phased / combined
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*/
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#include "bench_common.cuh"
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#include <cstdlib>
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// ============================================================
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// PriorityVRPProblem（复用 e2.1 定义）
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// ============================================================
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struct PriorityVRPProblem : ProblemBase<PriorityVRPProblem, 16, 64> {
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const float* d_dist;
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const float* d_demand;
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const int* d_priority;
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const float* h_dist;
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int n, stride;
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float capacity;
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int num_vehicles, max_vehicles;
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GpuCache cache;
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__device__ float compute_route_dist(const int* route, int size) const {
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if (size == 0) return 0.0f;
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float dist = 0.0f;
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int prev = 0;
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for (int j = 0; j < size; j++) {
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int node = route[j] + 1;
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dist += d_dist[prev * stride + node];
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prev = node;
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}
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dist += d_dist[prev * stride + 0];
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return dist;
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}
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__device__ float calc_total_distance(const Sol& sol) const {
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float total = 0.0f;
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for (int r = 0; r < num_vehicles; r++)
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total += compute_route_dist(sol.data[r], sol.dim2_sizes[r]);
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return total;
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}
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static constexpr ObjDef OBJ_DEFS[] = {{ObjDir::Minimize, 1.0f, 0.0f}};
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__device__ float compute_obj(int, const Sol& sol) const { return calc_total_distance(sol); }
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__device__ float compute_penalty(const Sol& sol) const {
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float pen = 0.0f;
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int active = 0;
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for (int r = 0; r < num_vehicles; r++) {
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int size = sol.dim2_sizes[r];
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if (size == 0) continue;
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active++;
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float load = 0.0f;
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for (int j = 0; j < size; j++) load += d_demand[sol.data[r][j]];
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if (load > capacity) pen += (load - capacity) * 100.0f;
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int min_prio_seen = 3;
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for (int j = 0; j < size; j++) {
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int p = d_priority[sol.data[r][j]];
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if (p > min_prio_seen) pen += (float)(p - min_prio_seen) * 50.0f;
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if (p < min_prio_seen) min_prio_seen = p;
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}
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}
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if (active > max_vehicles) pen += (float)(active - max_vehicles) * 1000.0f;
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return pen;
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}
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ProblemConfig config() const {
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ProblemConfig cfg;
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cfg.encoding = EncodingType::Permutation;
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cfg.dim1 = num_vehicles; cfg.dim2_default = 0;
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fill_obj_config(cfg);
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cfg.cross_row_prob = 0.3f;
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cfg.row_mode = RowMode::Partition;
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cfg.total_elements = n;
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return cfg;
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}
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static constexpr size_t SMEM_LIMIT = 48 * 1024;
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size_t shared_mem_bytes() const {
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size_t total = (size_t)stride*stride*sizeof(float) + (size_t)n*sizeof(float) + (size_t)n*sizeof(int);
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return total <= SMEM_LIMIT ? total : 0;
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}
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size_t working_set_bytes() const {
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return (size_t)stride*stride*sizeof(float) + (size_t)n*sizeof(float) + (size_t)n*sizeof(int);
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}
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__device__ void load_shared(char* smem, int tid, int bsz) {
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float* sd = reinterpret_cast<float*>(smem);
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int dist_size = stride * stride;
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for (int i = tid; i < dist_size; i += bsz) sd[i] = d_dist[i];
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d_dist = sd;
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float* sdem = sd + dist_size;
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for (int i = tid; i < n; i += bsz) sdem[i] = d_demand[i];
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d_demand = sdem;
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int* spri = reinterpret_cast<int*>(sdem + n);
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for (int i = tid; i < n; i += bsz) spri[i] = d_priority[i];
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d_priority = spri;
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}
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void init_relation_matrix(float* G, float* O, int N) const {
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if (!h_dist || N != n) return;
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float max_d = 0.0f;
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for (int i = 0; i < N; i++)
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for (int j = 0; j < N; j++) {
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float d = h_dist[(i+1)*stride+(j+1)];
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if (d > max_d) max_d = d;
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}
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if (max_d <= 0.0f) return;
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for (int i = 0; i < N; i++)
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for (int j = 0; j < N; j++) {
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if (i == j) continue;
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float d = h_dist[(i+1)*stride+(j+1)];
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float prox = 1.0f - d / max_d;
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G[i*N+j] = prox * 0.3f;
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O[i*N+j] = prox * 0.1f;
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}
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}
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static PriorityVRPProblem create(const float* h_dist_ptr, const float* h_demand,
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const int* h_priority, int n, float cap, int nv, int mv) {
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PriorityVRPProblem prob;
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prob.n = n; prob.stride = n+1; prob.capacity = cap;
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prob.num_vehicles = nv; prob.max_vehicles = mv;
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prob.cache = GpuCache::disabled(); prob.h_dist = h_dist_ptr;
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int nn = n+1;
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float* dd; CUDA_CHECK(cudaMalloc(&dd, sizeof(float)*nn*nn));
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CUDA_CHECK(cudaMemcpy(dd, h_dist_ptr, sizeof(float)*nn*nn, cudaMemcpyHostToDevice));
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prob.d_dist = dd;
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float* ddem; CUDA_CHECK(cudaMalloc(&ddem, sizeof(float)*n));
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CUDA_CHECK(cudaMemcpy(ddem, h_demand, sizeof(float)*n, cudaMemcpyHostToDevice));
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prob.d_demand = ddem;
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int* dpri; CUDA_CHECK(cudaMalloc(&dpri, sizeof(int)*n));
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CUDA_CHECK(cudaMemcpy(dpri, h_priority, sizeof(int)*n, cudaMemcpyHostToDevice));
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prob.d_priority = dpri;
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return prob;
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}
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void destroy() {
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if (d_dist) { cudaFree(const_cast<float*>(d_dist)); d_dist = nullptr; }
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if (d_demand) { cudaFree(const_cast<float*>(d_demand)); d_demand = nullptr; }
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if (d_priority) { cudaFree(const_cast<int*>(d_priority)); d_priority = nullptr; }
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h_dist = nullptr; cache.destroy();
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}
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};
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// ============================================================
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// VRPTW-20: 20 客户 4 车，紧时间窗
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// ============================================================
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// 坐标在 [0,100]x[0,100] 区域，depot 在中心 (50,50)
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// 时间窗故意设紧：窗口宽度 15-30，服务时间 5-10
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// 容量 50，需求 5-15 → 平均每车 5 客户，容量紧张
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static const int VRPTW20_N = 20;
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static const int VRPTW20_NODES = 21;
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static const float vrptw20_coords[VRPTW20_NODES][2] = {
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{50,50}, // depot
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{20,70},{35,80},{15,55},{40,65},{60,85},
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{75,70},{90,60},{80,45},{65,30},{50,20},
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{30,15},{15,30},{25,45},{45,40},{70,50},
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{85,75},{55,65},{35,35},{60,15},{80,25}
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};
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static const float vrptw20_demand[VRPTW20_N] = {
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8,12,7,10,15, 9,11,8,13,6, 10,14,7,12,9, 8,11,13,10,7
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};
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static const float vrptw20_earliest[VRPTW20_NODES] = {
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0, 5, 10, 0, 15, 20, 5, 25, 10, 0, 30,
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15, 0, 20, 10, 5, 25, 15, 0, 35, 20
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};
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static const float vrptw20_latest[VRPTW20_NODES] = {
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999, 25, 35, 20, 40, 50, 30, 55, 35, 25, 60,
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40, 25, 45, 35, 30, 55, 40, 25, 65, 45
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};
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static const float vrptw20_service[VRPTW20_NODES] = {
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0, 5,7,5,8,6, 7,5,8,6,5, 7,5,8,6,7, 5,8,6,7,5
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};
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// ============================================================
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// 50 客户随机实例生成（确定性种子）
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// ============================================================
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static void gen_random_coords(float coords[][2], int n_nodes, unsigned seed) {
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srand(seed);
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coords[0][0] = 50.0f; coords[0][1] = 50.0f;
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for (int i = 1; i < n_nodes; i++) {
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coords[i][0] = (float)(rand() % 100);
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coords[i][1] = (float)(rand() % 100);
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}
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}
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static void gen_random_demand(float* demand, int n, unsigned seed) {
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srand(seed + 1000);
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for (int i = 0; i < n; i++)
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demand[i] = 5.0f + (float)(rand() % 11); // [5, 15]
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}
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static void gen_random_priority(int* priority, int n, unsigned seed) {
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srand(seed + 2000);
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for (int i = 0; i < n; i++)
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priority[i] = rand() % 3; // 0, 1, 2
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}
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// ============================================================
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// 配置变体
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// ============================================================
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struct ConfigVariant {
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const char* name;
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bool constraint_directed;
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bool phased_search;
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};
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static const ConfigVariant VARIANTS[] = {
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{"baseline", false, false},
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{"constraint", true, false},
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{"phased", false, true},
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{"combined", true, true},
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};
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static const int NUM_VARIANTS = 4;
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static SolverConfig make_p2_config(float seconds, const ConfigVariant& v) {
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SolverConfig c = make_timed_config(seconds);
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c.use_constraint_directed = v.constraint_directed;
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c.use_phased_search = v.phased_search;
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return c;
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}
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// ============================================================
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// VRPTW-20 实验
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// ============================================================
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static void run_vrptw20() {
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fprintf(stderr, "\n=== VRPTW-20 (tight time windows) ===\n");
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float dist[VRPTW20_NODES * VRPTW20_NODES];
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for (int i = 0; i < VRPTW20_NODES; i++)
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for (int j = 0; j < VRPTW20_NODES; j++) {
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float dx = vrptw20_coords[i][0] - vrptw20_coords[j][0];
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float dy = vrptw20_coords[i][1] - vrptw20_coords[j][1];
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dist[i * VRPTW20_NODES + j] = sqrtf(dx*dx + dy*dy);
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}
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float budgets[] = {1.0f, 3.0f, 10.0f};
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for (float t : budgets) {
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for (int v = 0; v < NUM_VARIANTS; v++) {
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char cfg_name[64];
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snprintf(cfg_name, sizeof(cfg_name), "%s_%.0fs", VARIANTS[v].name, t);
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SolverConfig c = make_p2_config(t, VARIANTS[v]);
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bench_run_recreate("VRPTW-20", cfg_name,
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[&]() {
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return VRPTWProblem::create(
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dist, vrptw20_demand, vrptw20_earliest, vrptw20_latest,
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vrptw20_service, VRPTW20_N, 50.0f, 4, 4);
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},
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c, 0.0f);
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}
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}
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}
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// ============================================================
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// PrioVRP-50 实验
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// ============================================================
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static void run_prio_vrp50() {
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fprintf(stderr, "\n=== PrioVRP-50 (50 customers, priority constraints) ===\n");
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const int N = 50;
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const int NODES = N + 1;
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float coords[NODES][2];
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float demand[N];
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int priority[N];
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gen_random_coords(coords, NODES, 12345);
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gen_random_demand(demand, N, 12345);
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gen_random_priority(priority, N, 12345);
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float dist[NODES * NODES];
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for (int i = 0; i < NODES; i++)
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for (int j = 0; j < NODES; j++) {
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float dx = coords[i][0] - coords[j][0];
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float dy = coords[i][1] - coords[j][1];
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dist[i * NODES + j] = sqrtf(dx*dx + dy*dy);
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}
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float budgets[] = {1.0f, 3.0f, 10.0f};
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for (float t : budgets) {
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for (int v = 0; v < NUM_VARIANTS; v++) {
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char cfg_name[64];
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snprintf(cfg_name, sizeof(cfg_name), "%s_%.0fs", VARIANTS[v].name, t);
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SolverConfig c = make_p2_config(t, VARIANTS[v]);
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bench_run_recreate("PrioVRP-50", cfg_name,
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[&]() {
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return PriorityVRPProblem::create(
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dist, demand, priority, N, 60.0f, 8, 10);
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},
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c, 0.0f);
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}
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}
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}
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// ============================================================
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// VRP A-n32-k5 短时间（1s）— 验证短时间下是否有差异
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// ============================================================
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static void run_vrp_short() {
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fprintf(stderr, "\n=== VRP A-n32-k5 (short budget) ===\n");
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float dist[AN32K5_NODES * AN32K5_NODES];
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compute_euc2d_dist(dist, an32k5_coords, AN32K5_NODES);
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float budgets[] = {0.5f, 1.0f};
|
|||
|
|
for (float t : budgets) {
|
|||
|
|
for (int v = 0; v < NUM_VARIANTS; v++) {
|
|||
|
|
char cfg_name[64];
|
|||
|
|
snprintf(cfg_name, sizeof(cfg_name), "%s_%.1fs", VARIANTS[v].name, t);
|
|||
|
|
SolverConfig c = make_p2_config(t, VARIANTS[v]);
|
|||
|
|
bench_run_recreate("VRP-A32k5", cfg_name,
|
|||
|
|
[&]() { return VRPProblem::create(dist, an32k5_demands, AN32K5_N, 100.0f, 5, 5); },
|
|||
|
|
c, 784.0f);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
int main() {
|
|||
|
|
bench_init();
|
|||
|
|
bench_csv_header();
|
|||
|
|
run_vrptw20();
|
|||
|
|
run_prio_vrp50();
|
|||
|
|
run_vrp_short();
|
|||
|
|
fprintf(stderr, "\n[e8v2] P2 search strategy large-scale test completed.\n");
|
|||
|
|
return 0;
|
|||
|
|
}
|