收集和修改的PSO算法，可用于参考实现：

#include 
  
   
#include 
   
     #include 
    
      #include 
     
       #include 
      
        #define rand_01 ((float)rand() / (float)RAND_MAX) const int numofdims = 30; const int numofparticles = 50; using namespace std; //typedef void (*FitnessFunc)(float X[numofparticles][numofdims], float fitnesses[numofparticles]); void fitnessfunc(float X[numofparticles][numofdims], float fitnesses[numofparticles]) { memset(fitnesses, 0, sizeof (float) * numofparticles); for(int i = 0; i < numofparticles; i++) { for(int j = 0; j < numofdims; j++) { fitnesses[i] += X[i][j] * X[i][j]; //(pow(X[i][j], 2)); } } } void rosenBroekFunc(float X[numofparticles][numofdims], float fitnesses[numofparticles]) { float x1, x2, t1, t2; memset(fitnesses, 0, sizeof (float) * numofparticles); for(int i = 0; i < numofparticles; i++) for(int j = 0; j < numofdims - 1; j++) { x1 = X[i][j]; x2 = X[i][j+1]; t1 = (x2 - x1 * x1); t1 *= t1; t1 *= 100; t2 = x1 - 1; t2 *= t2; fitnesses[i] = t1 + t2; } } float mean(float inputval[], int vallength) { float addvalue = 0; for(int i = 0; i < vallength; i++) { addvalue += inputval[i]; } return addvalue / vallength; } void PSO(int numofiterations, float c1, float c2, float Xmin[numofdims], float Xmax[numofdims], float initialpop[numofparticles][numofdims], float worsts[], float meanfits[], float bests[], float *gbestfit, float gbest[numofdims]) { float V[numofparticles][numofdims] = {0}; float X[numofparticles][numofdims]; float Vmax[numofdims]; float Vmin[numofdims]; float pbests[numofparticles][numofdims]; float pbestfits[numofparticles]; float fitnesses[numofparticles]; float w; float minfit; int minfitidx; memcpy(X, initialpop, sizeof(float) * numofparticles * numofdims); fitnessfunc(X, fitnesses); //rosenBroekFunc(X, fitnesses); // fp(X, fitnesses); minfit = *min_element(fitnesses, fitnesses + numofparticles); minfitidx = min_element(fitnesses, fitnesses + numofparticles) - fitnesses; *gbestfit = minfit; memcpy(gbest, X[minfitidx], sizeof(float) * numofdims); //设置速度极限 for(int i = 0; i < numofdims; i++) { Vmax[i] = 0.2 * (Xmax[i] - Xmin[i]); Vmin[i] = -Vmax[i]; } for(int t = 0; t < 1000; t++) { w = 0.9 - 0.7 * t / numofiterations; //计算个体历史极小值 for(int i = 0; i < numofparticles; i++) { if(fitnesses[i] < pbestfits[i]) { pbestfits[i] = fitnesses[i]; //pbestfits初始化尚未赋值 memcpy(pbests[i], X[i], sizeof(float) * numofdims); } } for(int i = 0; i < numofparticles; i++) { for(int j = 0; j < numofdims; j++) { V[i][j] = min(max((w * V[i][j] + rand_01 * c1 * (pbests[i][j] - X[i][j]) + rand_01 * c2 * (gbest[j] - X[i][j])), Vmin[j]), Vmax[j]); X[i][j] = min(max((X[i][j] + V[i][j]), Xmin[j]), Xmax[j]); } } fitnessfunc(X, fitnesses); //rosenBroekFunc(X, fitnesses); minfit = *min_element(fitnesses, fitnesses + numofparticles); minfitidx = min_element(fitnesses, fitnesses + numofparticles) - fitnesses; if(minfit < *gbestfit) { *gbestfit = minfit; //cout << "It=" << t << "->" << minfit << endl; memcpy(gbest, X[minfitidx], sizeof(float) * numofdims); } worsts[t] = *max_element(fitnesses, fitnesses + numofparticles); bests[t] = *gbestfit; meanfits[t] = mean(fitnesses, numofparticles); } } int main() { time_t t; srand((unsigned) time(&t)); float xmin[30], xmax[30]; float initpop[50][30]; float worsts[1000], bests[1000]; float meanfits[1000]; float gbestfit; float gbest[30]; for(int i = 0; i < 30; i++) { xmax[i] = 100; xmin[i] = -100; } for(int i = 0; i < 50; i++) for(int j = 0; j < 30; j++) { initpop[i][j] = rand() % (100 + 100 + 1) - 100; } PSO(1000, 2, 2, xmin, xmax, initpop, worsts, meanfits, bests, &gbestfit, gbest); cout<<"fitness: " << gbestfit << endl; for(int i = 0; i < 30; i++) cout << gbest[i] << ", "; cout << endl; return 0; }