ABC 算法解决PFSP的代码

ABC 算法（Artificial Bee Colony Algorithm）是一种基于蜜蜂觅食行为的启发式优化算法，用于求解优化问题。下面是使用ABC算法解决PFSP（Permutation Flowshop Scheduling Problem）的代码示例：

import random
import numpy as np

# 定义PFSP问题
class PFSP:
    def __init__(self, jobs, machines, processing_times):
        self.jobs = jobs
        self.machines = machines
        self.processing_times = processing_times

    def evaluate(self, permutation):
        makespan = np.zeros((self.machines, len(self.jobs)))
        for i in range(len(permutation)):
            job = permutation[i]
            if i == 0:
                makespan[:, i] = makespan[:, i - 1] + self.processing_times[:, job]
            else:
                makespan[:, i] = np.maximum(makespan[:, i - 1], makespan[:, i]) + self.processing_times[:, job]
        return np.max(makespan)

# 定义ABC算法
class ABC:
    def __init__(self, pfsp, colony_size, max_iterations):
        self.pfsp = pfsp
        self.colony_size = colony_size
        self.max_iterations = max_iterations
        self.best_solution = None
        self.best_fitness = float('inf')
        self.solutions = []

    def generate_solution(self):
        permutation = np.arange(self.pfsp.jobs)
        np.random.shuffle(permutation)
        return permutation

    def evaluate_solution(self, solution):
        return self.pfsp.evaluate(solution)

    def search(self):
        for i in range(self.max_iterations):
            for j in range(self.colony_size):
                solution = self.generate_solution()
                fitness = self.evaluate_solution(solution)
                if fitness < self.best_fitness:
                    self.best_solution = solution
                    self.best_fitness = fitness
                self.solutions.append(solution)

            best_solution_index = np.argmin([self.evaluate_solution(s) for s in self.solutions])
            best_solution = self.solutions[best_solution_index]

            for k in range(self.colony_size):
                trial_solution = best_solution.copy()
                index = random.randint(0, self.pfsp.jobs - 1)
                trial_solution[index] = random.choice([i for i in range(self.pfsp.jobs) if i != trial_solution[index]])
                trial_fitness = self.evaluate_solution(trial_solution)
                if trial_fitness < self.best_fitness:
                    self.best_solution = trial_solution
                    self.best_fitness = trial_fitness

        return self.best_solution, self.best_fitness

# 示例使用
jobs = 5
machines = 3
processing_times = np.array([[2, 3, 1, 4, 2],
                            [3, 1, 4, 2, 3],
                            [4, 2, 3, 1, 4]])

pfsp = PFSP(jobs, machines, processing_times)
abc = ABC(pfsp, colony_size=20, max_iterations=100)
best_solution, best_fitness = abc.search()

print("Best solution:", best_solution)
print("Best fitness:", best_fitness)

上述代码中，首先定义了一个PFSP类用于表示PFSP问题，其中evaluate方法用于计算某个排列的最大完成时间。然后定义了一个ABC类，其中generate_solution方法用于生成随机解，evaluate_solution方法用于计算解的适应度。search方法是ABC算法的主要过程，其中包括了蜜蜂的搜索和跟随最优解的更新。最后，通过创建PFSP对象和ABC对象，并调用search方法，即可求解PFSP问题并输出结果。

请注意，以上代码仅为示例，实际应用中可能需要根据具体问题进行一些调整和优化。