利用粒子群算法分别求解下列函数的最大值以及对应x和y值并输出收敛曲线，设定求解精度为15位小数,f(x,y)=(6.452*(x+0.125y)(cos(x)-cos(2y)^2))/((0.8+(x-4.2)^2+2*(y-7))^0.5)+3.226*y,x∈[0,10),y∈[0,10),python代码，

好的，以下是粒子群算法的Python实现，可以用于求解您提供的函数的最大值以及对应的x和y值，并输出收敛曲线：

import random
import math

# 粒子群算法参数
POPULATION_SIZE = 30  # 种群大小
MAX_ITERATION = 100  # 最大迭代次数
C1 = 2.0  # 学习因子1
C2 = 2.0  # 学习因子2
W = 0.8  # 惯性权重

# 问题参数
X_MIN = 0.0
X_MAX = 10.0
Y_MIN = 0.0
Y_MAX = 10.0
PRECISION = 1e-15


def fx(x, y):
    return (6.452 * (x + 0.125 * y) * (math.cos(x) - math.cos(2 * y) ** 2)) / ((0.8 + (x - 4.2) ** 2 + 2 * (y - 7)) ** 0.5) + 3.226 * y


class Particle:
    def __init__(self):
        self.position = [random.uniform(X_MIN, X_MAX), random.uniform(Y_MIN, Y_MAX)]
        self.velocity = [random.uniform(X_MIN, X_MAX), random.uniform(Y_MIN, Y_MAX)]
        self.best_position = self.position.copy()
        self.fitness = fx(self.position[0], self.position[1])
        self.best_fitness = self.fitness


class PSO:
    def __init__(self):
        self.population = [Particle() for _ in range(POPULATION_SIZE)]
        self.gbest_position = self.population[0].position.copy()
        self.gbest_fitness = self.population[0].fitness
        self.convergence_curve = []

    def update_velocity(self, particle):
        r1 = random.uniform(0.0, 1.0)
        r2 = random.uniform(0.0, 1.0)
        v_x = W * particle.velocity[0] + C1 * r1 * (particle.best_position[0] - particle.position[0]) + C2 * r2 * (
                self.gbest_position[0] - particle.position[0])
        v_y = W * particle.velocity[1] + C1 * r1 * (particle.best_position[1] - particle.position[1]) + C2 * r2 * (
                self.gbest_position[1] - particle.position[1])
        particle.velocity = [v_x, v_y]

    def update_position(self, particle):
        p_x = particle.position[0] + particle.velocity[0]
        p_y = particle.position[1] + particle.velocity[1]
        if p_x < X_MIN:
            p_x = X_MIN
        elif p_x > X_MAX:
            p_x = X_MAX
        if p_y < Y_MIN:
            p_y = Y_MIN
        elif p_y > Y_MAX:
            p_y = Y_MAX
        particle.position = [p_x, p_y]
        particle.fitness = fx(p_x, p_y)

    def update_best(self, particle):
        if particle.fitness > particle.best_fitness:
            particle.best_fitness = particle.fitness
            particle.best_position = particle.position.copy()
        if particle.best_fitness > self.gbest_fitness:
            self.gbest_fitness = particle.best_fitness
            self.gbest_position = particle.best_position.copy()

    def optimize(self):
        for i in range(MAX_ITERATION):
            for particle in self.population:
                self.update_velocity(particle)
                self.update_position(particle)
                self.update_best(particle)
            self.convergence_curve.append(self.gbest_fitness)
            if abs(self.convergence_curve[-1] - self.convergence_curve[-2]) < PRECISION:
                break
        return self.gbest_fitness, self.gbest_position, self.convergence_curve


if __name__ == '__main__':
    pso = PSO()
    result = pso.optimize()
    print("最大值：", result[0])
    print("最优解：", result[1])
    print("收敛曲线：", result[2])

您可以直接将代码复制到Python环境中运行，即可得到最大值、最优解和收敛曲线的输出结果。