clpso算法python代码

CLPSO (Constrained Local Particle Swarm Optimization) 算法是一种结合了粒子群优化（Particle Swarm Optimization，PSO）和局部搜索策略的改进版本。它增强了PSO在解决约束优化问题时的能力。以下是一个简单的CLPSO算法的Python代码框架示例：

import numpy as np

class CLPSO:
    def __init__(self, obj_func, lb, ub, n_particles, max_iter, w=0.7, c1=2, c2=2, constr=None):
        self.obj_func = obj_func
        self.lb = lb
        self.ub = ub
        self.n_particles = n_particles
        self.max_iter = max_iter
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.constr = constr if constr else []
        
    def initialize(self):
        self.particles = np.random.uniform(self.lb, self.ub, (self.n_particles, len(self.lb)))
        self.velocities = np.zeros_like(self.particles)
        self.best_positions = self.particles.copy()
        self.global_best_position = None

    def update_velocity_and_pos(self):
        # 更新速度和位置的部分，包括惯性权重、个人最优和全局最优的影响
        # ...

    def check_constraints(self, position):
        # 检查当前位置是否违反约束
        return all(constraint(position) for constraint in self.constr)

    def run(self):
        for _ in range(self.max_iter):
            self.update_velocity_and_pos()
            new_positions = self.particles + self.velocities
            feasible_particles = [p for p in zip(new_positions, self.particles) if self.check_constraints(p[0])]
            updated_global_best = self.update_global_best(feasible_particles)
            
        return updated_global_best

# 示例：假设有一个函数f(x, y)作为目标函数
def f(x, y):
    return x**2 + y**2

clpso = CLPSO(f, [-5, -5], [5, 5], 50, 100)
solution = clpso.run()

请注意，这个代码只是一个简化的模板，并未包含完整的更新速度和位置以及约束检查的细节。实际应用中，你需要实现`update_velocity_and_pos`函数并处理好约束条件。