野狗优化算法python代码

野狗优化算法是一种基于种群的全局优化算法，其灵感来源于野狗捕食时的行为。这种算法的主要特点是通过不断的迭代和自适应调整来找到最优解。下面是野狗优化算法的Python代码示例：

import numpy as np

class WOA:
    def __init__(self, func, dim, max_iter=100, lb=-100, ub=100, population=10):
        self.func = func
        self.dim = dim
        self.max_iter = max_iter
        self.lb = lb
        self.ub = ub
        self.population = population

    def init_population(self):
        return np.random.uniform(low=self.lb, high=self.ub, size=(self.population, self.dim))

    def get_fitness(self, population):
        fitness = np.zeros(self.population)
        for i in range(self.population):
            fitness[i] = self.func(population[i])
        return fitness

    def update_position(self, population, leader_pos):
        a = 2 - 2 * np.linspace(0, 1, self.max_iter) ** 2
        r1 = np.random.rand(self.population, self.dim)
        r2 = np.random.rand(self.population, self.dim)
        A = 2 * a * r1 - population)
        new_population = leader_pos - A * D
        return new_population

    def run(self):
        population = self.init_population()
        fitness = self.get_fitness(population)
        best_index = np.argmin(fitness)
        best_fitness = fitness[best_index]
        best_position = population[best_index]
        for i in range(self.max_iter):
            a = 2 - 2 * (i / (self.max_iter - 1)) ** 2
            A = 2 * a * np.random.rand(self.dim) - a
            C = 2 * np.random.rand(self.dim)
            b = 1
            l = (a - 1) * np.random.rand() + 1
            p = np.random.rand()
            if p < 0.5:
                if np.abs(A) < 1:
                    new_population = self.update_position(population, best_position)
                else:
                    rand_leader_index = np.random.randint(0, self.population)
                    rand_leader_pos = population[rand_leader_index]
                    new_population = self.update_position(population, rand_leader_pos)
            else:
                distance_to_leader = np.abs(C * best_position - population)
                new_population = distance_to_leader * np.exp(b * l) * np.cos(2 * np.pi * l) + best_position
            new_population[new_population < self.lb] = self.lb
            new_population[new_population > self.ub] = self.ub
            new_fitness = self.get_fitness(new_population)
            new_best_index = np.argmin(new_fitness)
            if new_fitness[new_best_index] < best_fitness:
                best_fitness = new_fitness[new_best_index]
                best_position = new_population[new_best_index]
            population = new_population
            fitness = new_fitness
        return best_position, best_fitness

以上是一个简单的野狗优化算法的Python实现，需要提供目标函数、维度、最大迭代次数、上下界和种群大小等参数。在`run()`方法中进行算法的迭代，并返回最优解及其对应的函数值。