混合蛙跳算法求解设置选址实例的python代码

混合蛙跳算法是一种优化搜索策略，它结合了蛙跳法和模拟退火等随机搜索方法，用于解决复杂问题中的全局优化问题，比如旅行商问题、设施定位等问题。以下是使用Python实现的混合蛙跳算法的一个简单示例，这里以二位平面内的选址问题为例：

import numpy as np
import random

def normal_random(x):
    return np.random.normal(x, 0.5)

class HybridFrogLeaping:
    def __init__(self, problem_size, max_jumps, temp=0.9, cool_rate=0.95):
        self.size = problem_size
        self.max_jumps = max_jumps
        self.temp = temp
        self.cool_rate = cool_rate
        self.best_solution = None
        self.best_fitness = float('inf')

    def initial_population(self):
        solutions = [np.random.randint(0, self.size, size=self.size) for _ in range(self.size)]
        return solutions

    def fitness_function(self, solution):
        # 这里假设 fitness 函数是对平面上各个点距离的平方之和，越小越好
        return sum([solution[i]**2 + solution[(i+1)%self.size]**2 for i in range(self.size)])

    def frog_leap(self, current_position, steps):
        new_position = current_position.copy()
        for _ in range(steps):
            jump = (normal_random(2), normal_random(2))
            if abs(jump[0]) < 1 and abs(jump[1]) < 1:
                index = random.randint(0, self.size - 1)
                new_position[index] += jump[0]
                new_position[(index + 1) % self.size] += jump[1]
        return new_position

    def simulated_annealing(self, current_position):
        new_position = self.frog_leap(current_position, self.max_jumps)
        new_fitness = self.fitness_function(new_position)
        if new_fitness < self.best_fitness:
            self.best_solution = new_position
            self.best_fitness = new_fitness

        accept_prob = min(1, np.exp((self.best_fitness - new_fitness) / self.temp))
        if random.random() < accept_prob:
            self.best_solution = new_position
        self.temp *= self.cool_rate
        return new_position

    def hybrid_frog_leaping(self, iterations):
        population = self.initial_population()
        for _ in range(iterations):
            best_new_position = self.simulated_annealing(population[0])
            for position in population:
                new_position = self.simulated_annealing(position)
                if self.fitness_function(new_position) < self.fitness_function(best_new_position):
                    best_new_position = new_position

            population = [best_new_position] + population[1:]
        return self.best_solution

# 示例用法
problem_size = 10
iterations = 1000
solution = HybridFrogLeaping(problem_size, max_jumps=3).hybrid_frog_leaping(iterations)
print(f"Best Solution found: {solution}")