Cellular Multi-Objective Particle Swarm Algorithm Based on Multi-Strategy Differential Evolution

ZHU Da-lin; ZHAN Teng; ZHANG Yi; ZHENG Xiao-dong

doi:10.3969/j.issn.0372-2112.2014.09.026

您当前的位置：

首页 >

文章列表页 >

Cellular Multi-Objective Particle Swarm Algorithm Based on Multi-Strategy Differential Evolution

更新时间：2025-07-16

- Cellular Multi-Objective Particle Swarm Algorithm Based on Multi-Strategy Differential Evolution
- Acta Electronica Sinica Vol. 42, Issue 9, Pages: 1831-1838(2014)
- 作者机构：
  
  三峡大学水电机械设备设计与维护湖北省重点实验室,湖北,宜昌,443002
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51275274)
- DOI：10.3969/j.issn.0372-2112.2014.09.026
  CLC： TP38
- Published：2014
- 稿件说明：
移动端阅览
ZHU Da-lin, ZHAN Teng, ZHANG Yi, et al. Cellular Multi-Objective Particle Swarm Algorithm Based on Multi-Strategy Differential Evolution[J]. Acta Electronica Sinica, 2014, 42(9): 1831-1838.
DOI：

ZHU Da-lin, ZHAN Teng, ZHANG Yi, et al. Cellular Multi-Objective Particle Swarm Algorithm Based on Multi-Strategy Differential Evolution[J]. Acta Electronica Sinica, 2014, 42(9): 1831-1838. DOI： 10.3969/j.issn.0372-2112.2014.09.026.

摘要

为了增加Pareto解集的多样性，平衡多目标优化的全局搜索和局部寻优的能力，提出一种多策略差分进化的元胞多目标粒子群算法.该算法在分析粒子群优化原理基础上，将元胞自动机理论融入粒子群算法，研究粒子种群的交流结构和信息传递机制.为了避免粒子飞行速度过快陷入局部收敛，提出一种限制粒子飞行速度的策略，并引入一种多策略差分进化选择算子增加对粒子的扰动.实验证明，该算法相对于比较算法，有更好的收敛性和多样性.

Abstract

In order to strengthen the diversity of Pareto sets obtained by multi-objective optimization algorithms and balance the exploration and exploitation of the algorithms

a cellular multi-objective particle swarm optimization algorithm based on multi-strategy differential evolution (MPSOCell) is proposed.This algorithm is composed by integrating the cellular automate mechanism into the basic particle swarm optimization algorithm

and it is aimed at promoting the communication and information transmission among the particles.To avoid the local convergence caused by the fast flying speed of particles

a strategy used to limit the flying speed is designed;to strengthen the disturbance to the particles

a multi-strategy differential evolution operator is also brought into the algorithm.The experiments demonstrate that MPSOCell has better performance in terms of convergence and diversity.

关键词

Keywords

references

Views

1772

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Multi-Objective Particle Swarm Optimization Algorithm for Solving Negotiation Deadlock

Multi-objective Evolutionary Algorithm Based on Dynamic Population Multi-strategy Differential Models

Adaptive Cellular Differential Evolutionary Algorithm Based on Multi-neighborhood Structure

Comprehensive Causality Multi-Objective Counterfactual Explanation with Feature Selection

Yaw Stability Control of Distributed Drive Electric Vehicle Based on Multi-Objective Parallel Chaos Optimization

Related Author

PENG Zhi-ping

CHEN Ke

WANG Ya-hui

WU Jin-mei

JIA Chen-hui

WANG Ya-liang

NI Chen-di

JIN Shou-song

Related Institution

Department of Computer Science and Technology,Maoming College

School of Mechanical Engineering, North China University of Water Resources and Electric Power

School of Mechatronics Engineering, Henan University of Science and Technology

School of Mechanical Engineering North China University of Water Resources and Electric Power Zhengzhou Henan China

School of Mechatronics Engineering Henan University of Science and Technology Luoyang Henan China

⁰