dpso粒子群python代码

DPSO (Differential Evolution with Particle Swarm Optimization) 是一种结合了差分进化（DE）和粒子群优化（PSO）思想的混合智能优化算法。在Python中实现DPSO，你可以利用已有的库如`scipy`或者`deap`等来构建基本框架，下面是一个简化的示例：

import numpy as np
from deap import base, creator, tools

# 定义适应体和评价函数
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def evaluate(individual):
    # 这里假设individual是一个包含若干参数的列表，根据实际问题定义合适的评价函数
    return -individual[0]**2 - individual[1]**2,  # 返回负的目标函数值便于最大化

# 初始化粒子群参数
pop_size = 50
dimensions = 2
maxiter = 100

# 差分进化部分
toolbox = base.Toolbox()
toolbox.register("solution", creator.Individual, np.random.uniform(-10, 10, dimensions))
toolbox.register("population", tools.initRepeat, list, toolbox.solution)
toolbox.register("evaluate", evaluate)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
toolbox.register("select", tools.selTournament, tournsize=3)

# 粒子群优化部分
toolbox.register("update_velocity", tools.vectorized(lambda pbest, gbest, v, w, c1, c2: w * v + c1 * np.random.uniform() * (pbest - individual) + c2 * np.random.uniform() * (gbest - individual)))
toolbox.register("update_position", lambda i, ind, lb, ub: [max(lb, min(ub, x)) for x in ind])

# 主循环
pop = toolbox.population(n=pop_size)
for gen in range(maxiter):
    fitnesses = toolbox.map(toolbox.evaluate, pop)
    for ind, fit in zip(pop, fitnesses):
        ind.fitness.values = fit

    best_ind = tools.selBest(pop, k=1)[0]
    g_best = toolbox.clone(best_ind)

    velocities = [toolbox.update_velocity(pbest=p, gbest=g_best, v=v, w=w, c1=0.8, c2=0.9) for p, v in zip(pop, [ind.fitness.values for ind in pop])]
    
    positions = toolbox.map(toolbox.update_position, range(len(pop)), pop, [-10, 10], [-10, 10])
    pop[:] = [toolbox.clone(ind) for ind in positions]

# 输出结果
print(f"Best individual: {best_ind}")
print(f"Best fitness: {best_ind.fitness.values[0]}")