基于分工和模糊控制的粒子群算法

李金; 张纪会; 高学柳; 张保华; 李金; 张纪会; 高学柳; 张保华

doi:10.13306/j.1672-3813.2024.01.014

复杂系统与复杂性科学 >

2024 , Vol. 21 >Issue 1: 109 - 118

DOI: https://doi.org/10.13306/j.1672-3813.2024.01.014

研究论文

基于分工和模糊控制的粒子群算法

李金 ,
张纪会 ,
高学柳 ,
张保华 ,
李金 ,
张纪会 ,
高学柳 ,
张保华

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1.青岛大学 a.复杂性科学研究所;b.山东省工业控制技术重点实验室,山东青岛 266071;
2.青岛港国际股份有限公司,山东青岛 266011

李金(1996-),男,山东泰安人,硕士研究生,主要研究方向为智能优化方法。

收稿日期: 2022-10-16

修回日期: 2022-11-17

网络出版日期: 2024-04-26

基金资助

国家自然科学基金(61673228,62072260);青岛市科技计划(21-1-2-16-zhz)

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Particle Swarm Optimization Algorithm Based on Labor Division and Fuzzy Control

LI Jin ,
ZHANG Jihui ,
GAO Xueliu ,
ZHANG Baohua ,
LI Jin ,
ZHANG Jihui ,
GAO Xueliu ,
ZHANG Baohua

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1. a. Institute of Complexity Science; b. Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China;
2. Qingdao Port International Company, Ltd, Qingdao 266011, China

Received date: 2022-10-16

Revised date: 2022-11-17

Online published: 2024-04-26

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摘要

为解决粒子群算法在求解复杂优化问题时容易陷入局部最优、寻优精度低、后期收敛慢等问题,提出一种基于分工和模糊控制的粒子群算法,使用分工、参数自适应调整和融合距离因素的模拟退火三种策略对粒子群算法进行改进。将粒子分为侦察粒子和后卫粒子,侦察粒子负责进行探索,后卫粒子向个体最优解和全局最优解学习,保证种群多样性并加快搜索速度;使用Sigmoid函数调节惯性权重,模糊逻辑控制学习因子,以平衡算法的探索和开发能力;以模拟退火机制更新全局最优粒子,同时兼顾距离因素,增强算法跳出局部最优解的能力。采用25个标准测试函数进行仿真实验,仿真结果表明,改进算法在收敛精度、速度和稳定性上都有更好表现。

关键词： 群体智能; 粒子群算法; 个体最优更新率; 分工策略; 模糊逻辑; 模拟退火

本文引用格式

李金 , 张纪会 , 高学柳 , 张保华 , 李金 , 张纪会 , 高学柳 , 张保华 . 基于分工和模糊控制的粒子群算法[J]. 复杂系统与复杂性科学, 2024 , 21(1) : 109 -118 . DOI: 10.13306/j.1672-3813.2024.01.014

Abstract

In order to overcome the shortages of the particle swarm optimization algorithm, such as low accuracy, slow convergence and falling into local optima, a particle swarm optimization algorithm based on labor division and fuzzy control is proposed, which improves the algorithm by using the division of labor, parameter adaptive adjustment and simulated annealing with distance factors. Particles are divided into scout and rearguard ones, the former searches randomly and the latter learns from the best individual solutions as well as the best global solution to ensure the diversity of population and to accelerate the search. A sigmoid function is used to adjust the inertial weight and fuzzy logic is applied to balance exploration and exploitation capability of the algorithm. The best global particle is updated according to simulated annealing with distance factors taken into account, which improves the ability of the algorithms to jump out of the local optima. Simulation experiments on 25 standard test functions show that the improved algorithm has better performance in terms of convergence accuracy, speed and stability.

Key words： swarm intelligence; particle swarm optimization; individual optimal update rate; division of labor; fuzzy logic; simulated annealing

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