python实现双种群遗传算法详细代码

Python实现双种群遗传算法（Differential Evolution，DE）通常涉及到以下几个步骤：

1. **初始化种群**：创建两个大小相同的种群，每个种群包含一系列随机生成的解决方案（个体），称为“解向量”。

import numpy as np

def initialize_population(pop_size, problem_dim):
    pop1 = np.random.uniform(lower_bound, upper_bound, (pop_size, problem_dim))
    pop2 = np.random.uniform(lower_bound, upper_bound, (pop_size, problem_dim))
    return pop1, pop2

2. **适应度计算**：根据目标函数对每个解向量评估其适应度。

def fitness_function(solution):
    # 根据具体优化问题的函数定义
    return objective_function(solution)

3. **变异操作**：从两个种群中选择个体，并进行变异生成新的解向量。

def mutation(pop1, pop2, F):
    for i in range(pop1.shape[0]):
        a, b, c = select_individuals(pop1, pop2)
        mutant = a + F * (b - c)
        if check_boundary(mutant):  # 遵守边界条件
            pop1[i] = mutant

4. **交叉操作**：结合两个种群进行重组。

def crossover(pop1, pop2, CR):
    offspring1, offspring2 = [], []
    for _ in range(pop1.shape[0]):
        parent1, parent2 = select_parents(pop1, pop2)
        if np.random.rand() < CR:
            child = crossover_operator(parent1, parent2)
        else:
            child = parent1
        offspring1.append(child)
        offspring2.append(crossover_operator(parent2, parent1))  # 对称交叉
    return offspring1, offspring2

5. **更新种群**：将新产生的解替换到原种群中的一部分位置。

def update_population(pop1, pop2, offspring1, offspring2):
    elite = select_best(pop1, pop2)  # 保留精英个体
    pop1[elite_index] = offspring1
    pop2[elite_index] = offspring2

6. **迭代过程**：重复上述步骤直到达到预设的停止条件（如达到最大迭代次数或适应度收敛）。

for iteration in range(max_iterations):
    mutated_pop1, mutated_pop2 = mutation(pop1, pop2, F)
    crossed_offspring1, crossed_offspring2 = crossover(mutated_pop1, mutated_pop2, CR)
    update_population(pop1, pop2, crossed_offspring1, crossed_offspring2)

best_solution = select_best(pop1, pop2)

完整的代码示例：

class DE:
    def __init__(self, lower_bound, upper_bound, pop_size, max_iterations, F, CR):
        self.lower_bound, self.upper_bound = lower_bound, upper_bound
        self.pop_size = pop_size
        self.max_iterations = max_iterations
        self.F = F
        self.CR = CR
        self.pop1, self.pop2 = initialize_population(self.pop_size, problem_dim)

    def run(self):
        # ... 执行遗传算法的迭代过程 ...

if __name__ == "__main__":
    de_algorithm = DE(...args...)
    best_solution = de_algorithm.run()