改进的免疫算法在物流中心选址问题代码

以下是改进的免疫粒子群算法在物流中心选址问题的Python代码示例。这里使用的是基于Python的PyGMO优化库。

import pygmo as pg
import numpy as np

# 定义问题类
class LogisticsCenterProblem:
    def __init__(self, num_centers, num_customers, max_distance):
        self.num_centers = num_centers
        self.num_customers = num_customers
        self.max_distance = max_distance

    def fitness(self, x):
        # 计算适应度函数值
        total_distance = 0.0
        for i in range(self.num_customers):
            min_distance = float('inf')
            for j in range(self.num_centers):
                distance = np.linalg.norm(x[j] - x[self.num_centers+i])
                if distance < min_distance:
                    min_distance = distance
            total_distance += min_distance
        return [total_distance]

    def get_bounds(self):
        # 定义决策变量的取值范围
        return (np.zeros(self.num_centers*2), np.ones(self.num_centers*2)*self.max_distance)

    def get_name(self):
        # 定义问题名称
        return "Logistics Center Location Problem"

# 定义算法类
class ImprovedImmunePSO:
    def __init__(self, num_centers, num_customers, max_distance):
        self.num_centers = num_centers
        self.num_customers = num_customers
        self.max_distance = max_distance

    def optimize(self):
        # 定义问题
        problem = pg.problem(LogisticsCenterProblem(self.num_centers, self.num_customers, self.max_distance))
        # 定义算法
        algorithm = pg.algorithm(pg.immune_pso(gen=100, omega=0.5, eta1=1, eta2=1, m=20, n=20, variant=1))
        # 定义种群
        population = pg.population(problem, size=50)
        # 进行优化
        population = algorithm.evolve(population)
        # 返回最优解
        best_fitness = population.champion_f[0]
        best_solution = population.champion_x[:self.num_centers]
        return (best_fitness, best_solution)

# 测试算法
if __name__ == '__main__':
    num_centers = 5
    num_customers = 20
    max_distance = 100.0
    optimizer = ImprovedImmunePSO(num_centers, num_customers, max_distance)
    best_fitness, best_solution = optimizer.optimize()
    print("Best Fitness:", best_fitness)
    print("Best Solution:", best_solution)

需要注意的是，这里的代码仅供参考，具体实现方式可能因问题的不同而有所差异。如果您有任何问题或疑问，欢迎随时向我提问。