演化计算-组合式差分进化（CODE）算法python实现

话不多说，直接上代码：

import numpy as np
import random

class Population:
    def __init__(self, min_range, max_range, dim, factor, rounds, size, object_func, CR):
        self.min_range = min_range
        self.max_range = max_range
        self.dimension = dim
        self.factor = factor
        self.rounds = rounds
        self.size = size
        self.cur_round = 1
        self.CR = CR
        self.get_object_function_value = object_func
        # 初始化种群
        self.individuality = [np.array([random.uniform(self.min_range, self.max_range) for s in range(self.dimension)]) for tmp in range(size)]
        self.object_function_values = [self.get_object_function_value(v) for v in self.individuality]
        self.mutant = None
 
    def mutate1(self):
        self.mutant = []
        for i in range(self.size):
            r0, r1, r2 = 0, 0, 0
            while r0 == r1 or r1 == r2 or r0 == r2 or r0 == i:
                r0 = random.randint(0, self.size-1)
                r1 = random.randint(0, self.size-1)
                r2 = random.randint(0, self.size-1)
            tmp = self.individuality[r0] + (self.individuality[r1] - self.individuality[r2]) * self.factor
            for t in range(self.dimension):
                if tmp[t] > self.max_range or tmp[t] < self.min_range:
                    tmp[t] = random.uniform(self.min_range, self.max_range)
            self.mutant.append(tmp)
            
    def mutate2(self):
        self.mutant = []
        for i in range(self.size):
            r0, r1, r2, r3, r4 = 0, 0, 0, 0, 0
            while r0 == r1 or r1 == r2 or r0 == r2 or r0 == i or r0==r3 or r0==r4 or r2==r3 or r2==r4 or r3==r4:
                r0 = random.randint(0, self.size-1)
                r1 = random.randint(0, self.size-1)
                r2 = random.randint(0, self.size-1)
                r3 = random.randint(0, self.size-1)
                r4 = random.randint(0, self.size-1)
            tmp = self.individuality[r0] + (self.individuality[r1] - self.individuality[r2]) * self.factor + (self.individuality[r3] - self.individuality[r4]) * self.factor
            for t in range(self.dimension):
                if tmp[t] > self.max_range or tmp[t] < self.min_range:
                    tmp[t] = random.uniform(self.min_range, self.max_range)
            self.mutant.append(tmp)
            
    def mutate3(self):
        self.mutant = []
        for i in range(self.size):
            r0, r1, r2 = 0, 0, 0
            while r0 == r1 or r1 == r2 or r0 == r2 or r0 == i:
                r0 = random.randint(0, self.size-1)
                r1 = random.randint(0, self.size-1)
                r2 = random.randint(0, self.size-1)

            m = min(self.object_function_values)
            i = self.object_function_values.index(m)
            tmp = self.individuality[r0] + (self.individuality[r1] - self.individuality[r2]) * self.factor + (self.individuality[i] - self.individuality[r0]) * self.factor
            for t in range(self.dimension):
                if tmp[t] > self.max_range or tmp[t] < self.min_range:
                    tmp[t] = random.uniform(self.min_range, self.max_range)
            self.mutant.append(tmp)
 
    def crossover_and_select(self):
        for i in range(self.size):
            Jrand = random.randint(0, self.dimension)
            for j in range(self.dimension):
                if random.random() > self.CR and j != Jrand:
                    self.mutant[i][j] = self.individuality[i][j]
                tmp = self.get_object_function_value(self.mutant[i])
                if tmp < self.object_function_values[i]:
                    self.individuality[i] = self.mutant[i]
                    self.object_function_values[i] = tmp
 
 
    def evolution1(self):
        while self.cur_round < self.rounds:
            self.mutate1()
            self.crossover_and_select()
            self.cur_round = self.cur_round + 1
        m = min(self.object_function_values)
        i = self.object_function_values.index(m)
        print("最佳个体：" + str(self.individuality[i]))
        print("目标函数值：" + str(m))
        
    def evolution2(self):
        while self.cur_round < self.rounds:
            self.mutate2()
            self.crossover_and_select()
            self.cur_round = self.cur_round + 1
        m = min(self.object_function_values)
        i = self.object_function_values.index(m)
        print("最佳个体：" + str(self.individuality[i]))
        print("目标函数值：" + str(m))
        
    def evolution3(self):
        while self.cur_round < self.rounds:
            self.mutate3()
            self.crossover_and_select()
            self.cur_round = self.cur_round + 1
        m = min(self.object_function_values)
        i = self.object_function_values.index(m)
        print("最佳个体：" + str(self.individuality[i]))
        print("目标函数值：" + str(m))
 
#测试部分
if __name__ == "__main__":
    def f(v):
        f=0
        for i in v:
            f+=i**2
        return f
    #三种参数选择策略（随机选择）;p1,p2,p3的差异在于factor和CR
    p1 = Population(min_range=-10, max_range=10, dim=30, factor=1, rounds=800, size=100, object_func=f,CR=0.1)
    p2 = Population(min_range=-10, max_range=10, dim=30, factor=1, rounds=800, size=100, object_func=f,CR=0.9)
    p3 = Population(min_range=-10, max_range=10, dim=30, factor=0.8, rounds=800, size=100, object_func=f,CR=0.2)
    ps=[p1,p2,p3]
    def random_(ps):
        a=random.randint(0,2)
        if a==0:
            print('参数 F=1, CR=0.1')
        elif a==1:
            print('参数 F=1, CR=0.9')
        else:
            print('参数 F=0.8, CR=0.2')
        p=ps[a]
        return p
    for i in range(3):
        p=random_(ps)
        if i==0:
            print('变异策略1结果：')
            p.evolution1()
        elif i==1:
            print('变异策略2结果：')
            p.evolution2()
        else:
            print('变异策略3结果：')
            p.evolution3()