人工鱼群算法python

以下是Python实现的人工鱼群算法的示例代码：

import random
import math

class Fish:
    def __init__(self, dim, lower_bound, upper_bound, func):
        self.position = [random.uniform(lower_bound[i], upper_bound[i]) for i in range(dim)]
        self.fitness = func(self.position)

    def __str__(self):
        return f'Fish: {self.position}, Fitness: {self.fitness}'

class FishSwarm:
    def __init__(self, dim, num_fish, func, max_iter, visual, step_size, try_number):
        self.dim = dim
        self.num_fish = num_fish
        self.func = func
        self.max_iter = max_iter
        self.visual = visual
        self.step_size = step_size
        self.try_number = try_number
        self.lower_bound = [-100] * dim
        self.upper_bound = [100] * dim
        self.fishes = [Fish(dim, self.lower_bound, self.upper_bound, func) for _ in range(num_fish)]
        self.best_fish = self.get_best_fish()

    def get_best_fish(self):
        return min(self.fishes, key=lambda fish: fish.fitness)

    def move_towards_target(self, fish, target):
        direction = [target.position[i] - fish.position[i] for i in range(self.dim)]
        distance = math.sqrt(sum(d ** 2 for d in direction))
        direction = [d / distance for d in direction]
        new_position = [fish.position[i] + self.step_size * direction[i] * random.uniform(0, self.visual) for i in range(self.dim)]
        new_fitness = self.func(new_position)
        return Fish(self.dim, self.lower_bound, self.upper_bound, self.func) if new_fitness > fish.fitness else Fish(self.dim, self.lower_bound, self.upper_bound, self.func)

    def try_to_follow(self, fish):
        neighbours = sorted(self.fishes, key=lambda f: math.sqrt(sum((f.position[i] - fish.position[i]) ** 2 for i in range(self.dim))))[1:self.try_number+1]
        target = min(neighbours, key=lambda f: f.fitness)
        return self.move_towards_target(fish, target)

    def swim(self):
        for i in range(self.max_iter):
            new_fishes = []
            for fish in self.fishes:
                new_fish = self.try_to_follow(fish)
                new_fishes.append(new_fish)
            self.fishes = new_fishes
            self.best_fish = self.get_best_fish()
            print(f'Iteration {i+1}: {self.best_fish}')

这里提供了一个 Fish 类来表示鱼的位置和适应度，以及 FishSwarm 类来表示整个人工鱼群的状态和操作。在 `__init__` 函数中，我们初始化了一个初始的鱼群，并且记录了鱼的数量，维度，目标函数，最大迭代次数，视野范围，步长和尝试次数等参数。在 `move_towards_target` 函数中，我们计算了鱼到目标点的方向和距离，并根据步长和视野范围来计算新的位置。在 `try_to_follow` 函数中，我们选择了附近的几条鱼，找到其中适应度最好的一条，并调用 `move_towards_target` 函数来让当前鱼向这个目标点移动。在 `swim` 函数中，我们进行了多次迭代，每次迭代中，我们将每条鱼都尝试向适应度更好的鱼移动，并更新了整个鱼群的状态。

注意，上述代码仅为示例，实际使用时可能需要对其进行修改和优化。