差分进化算法求解平方和函数最小值.zip_3HQ_Y3V_差分进化_差分进化算法_差分进化算法求解平方和函数最小值

#include<stdlib.h> #include<time.h> #include<math.h> #include<iostream> using namespace std; const int upper_bound = 30;//变量上界 const int lower_bound = 0;//变量下界 const int NP = 5;//种群规模 const int D = 1;//变量维数 const int NG = 100;//最大进化代数 const double F = 0.4;//变异算子 const double CR = 0.1;//交叉算子 int random_integer(int a, int b);//产生a和b之间的随机整数 double random_real_number(int a, int b);//产生a和b之间的随机实数 double objective_function(double x);//目标函数 struct Individual //个体类 { double element[D];//个体元素值 double fitness_value;//对应个体的目标函数值 }; class DE //DE算法类 { private: int best_i;//当前种群中的最优个体（第几个） double best_fitness;//当前最优个体的适应值 int random_a;//随机数a int random_b;//随机数b int random_c;//随机数c double parent_fitness[NP];//父代种群的适应值 double childen_fitness[NP];//子代种群的适应值 public: DE(); ~DE(){} void initial_population(Individual *initial);//产生初始种群 void mutation_operator(Individual *initial, Individual *mutation);//变异 void crossover_operator(Individual *initial, Individual *mutation, Individual *crossover);//交叉 void select_operator(Individual *initial, Individual *crossover);//选择 void find_best_individual();//找到最优解 void output_best();//输出最优解 }; int random_integer(int a, int b)//产生a和b之间的随机整数 { int number = 0; if (a < b) number = rand() % (b - a) + a; else number = rand() % (a - b) + b; return number; } double random_real_number(int a, int b)//产生a和b之间的随机实数 { double number = 0; if (a < b) number = a + (b - a)*rand() / (RAND_MAX + 1.0); else if (a>b) number = b + (a - b)*rand() / (RAND_MAX + 1.0); else number = (double)a; return number; } double objective_function(double x)//目标函数 { return x*x*x - 60*x*x + 900*x + 100; } DE::DE() { best_i = 0; best_fitness = 0; random_a = 0; random_b = 0; random_c = 0; parent_fitness[NP] = { 0 }; childen_fitness[NP] = { 0 }; } void DE::initial_population(Individual *initial)//产生初始种群 { for (int i = 0; i < NP; i++) { for (int j = 0; j < D; j++) { initial[i].element[j] = random_real_number(upper_bound, lower_bound);//随机产生个体 initial[i].fitness_value = objective_function(initial[i].element[j]);//计算个体适应值 } } for (int i = 0; i < NP; i++)//将初始种群作为父代种群 parent_fitness[i] = initial[i].fitness_value; } void DE::mutation_operator(Individual *initial, Individual *mutation)//变异 { for (int i = 0; i < NP; i++) { do //变异的个体随机选择，但不能是同一个个体 { random_a = random_integer(0, NP); random_b = random_integer(0, NP); random_c = random_integer(0, NP); } while (i == random_a || i == random_b || i == random_c || random_a == random_b || random_a == random_b || random_b == random_c); for (int j = 0; j < D; j++)//变异操作 { mutation[i].element[j] = initial[random_c].element[j] + F*(initial[random_a].element[j] - initial[random_b].element[j]); if (mutation[i].element[j] > upper_bound || mutation[i].element[j] < lower_bound)//边界处理，如果变异后的个体不合要求，就重新产生 { mutation[i].element[j] = random_real_number(upper_bound, lower_bound); } } } } void DE::crossover_operator(Individual *initial, Individual *mutation, Individual *crossover)//交叉 { for (int i = 0; i < NP; i++) { for (int j = 0; j < D; j++) { if (random_real_number(0, 1) < CR) { crossover[i].element[j] = mutation[i].element[j]; } else { crossover[i].element[j] = initial[i].element[j]; } } } } void DE::select_operator(Individual *initial, Individual *crossover)//选择 { for (int i = 0; i < NP; i++) { for (int j = 0; j < D; j++) { if (objective_function(crossover[i].element[j]) >= objective_function(initial[i].element[j]))//更新个体 { initial[i].element[j] = crossover[i].element[j]; childen_fitness[i] = objective_function(crossover[i].element[j]); } else { childen_fitness[i] = parent_fitness[i]; } } } } void DE::find_best_individual()//找到最优解 { best_fitness = parent_fitness[0]; for (int i = 0; i < NP; i++) { if (best_fitness < parent_fitness[i]) { best_i = i; best_fitness = parent_fitness[i]; } } } void DE::output_best()//输出最优解 { cout << "最优解：" << parent_fitness[best_i] << endl; } int main() { int ng = 0; Individual _initial[NP];//当前种群 Individual _mutation[NP];//变异种群 Individual _crossover[NP];//交叉种群 DE de; clock_t start, finish; start = clock(); srand((unsigned)time(0)); de.initial_population(_initial); while (ng <= NG) { de.find_best_individual(); de.mutation_operator(_initial, _mutation); de.crossover_operator(_initial, _mutation, _crossover); de.select_operator(_initial, _crossover); ng++; } de.output_best(); finish = clock(); cout << double(finish - start) / CLOCKS_PER_SEC << " (s) " << endl; system("pause"); return 0; }