Particle swarm optimization algorithm with fast convergence and adaptive escape

doi:10.3724/SP.J.1087.2013.01308

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (05): 1308-1312.DOI: 10.3724/SP.J.1087.2013.01308

• Artificial intelligence • Previous Articles Next Articles

Particle swarm optimization algorithm with fast convergence and adaptive escape

SHI Xiaolu¹,SUN Hui²,LI Jun¹,ZHU Degang¹

1. School of Information Engineering, Nanchang Hangkong University, Nanchang Jiangxi 330063, China
2. School of Information Engineering, Nanchang Institute of Technology, Nanchang Jiangxi 330099, China

Received:2012-10-29 Revised:2012-12-13 Online:2013-05-01 Published:2013-05-08
Contact: SUN Hui
Supported by:
;Project supported by the natural science foundation of Jiangxi Province

具有快速收敛和自适应逃逸功能的粒子群优化算法

史小露¹,孙辉²,李俊¹,朱德刚¹

1. 南昌航空大学信息工程学院，南昌 330063
2. 南昌工程学院信息工程学院，南昌 330099

通讯作者: 孙辉
作者简介:史小露(1987-)，女，山西临汾人，硕士研究生，主要研究方向：群智能优化算法；孙辉(1959-)，男，江西九江人，教授，博士，主要研究方向：智能计算、Rough集与粒计算、变分不等原理与变分不等式；李俊(1987-)，男，江西赣州人，硕士研究生，主要研究方向：群智能优化算法；朱德刚(1988-)，男，安徽芜湖人，硕士研究生，主要研究方向：群智能优化算法。
基金资助:
国家自然科学基金资助项目(61261039);江西省自然科学基江西省自然科学基金资助项目(20122BAB201043)金资助项目

Abstract

Abstract: In order to overcome the drawbacks of Particle Swarm Optimization (PSO) that converges slowly at the last stage and easily falls into local minima, this paper proposed a new PSO algorithm with convergence acceleration and adaptive escape (FAPSO) inspired by the Artificial Bee Colony (ABC) algorithm. For each particle, FAPSO conducted two search operations. One was global search and the other was local search. When a particle got stuck, the adaptive escape operator was used to search the particle again. Experiments were conducted on eight classical benchmark functions. The simulation results demonstrate that the proposed approach improves the convergence rate and solution accuracy, when compared with some recently proposed PSO versions, such as CLPSO. Besides, the results of t-test show clear superiority.

Key words: Particle Swarm Optimization (PSO), global search, local search, fast convergence, adaptive escape

摘要： 为了克服标准粒子群优化算法(PSO)后期收敛速度慢、容易陷入局部最优等缺点，借鉴人工蜂群算法的思想，提出了一种提高收敛速度并且带有自适应逃逸功能的粒子群优化算法(FAPSO)。算法中每进化一次粒子搜索两次：一次全局搜索，一次局部搜索。当粒子陷入局部最优时，通过逃逸功能使粒子重新搜索。8个经典基准测试函数仿真结果表明，改进的粒子群优化算法在收敛速度和寻优精度上均有提高，相对于目前常用的改进粒子群优化算法如CLPSO等，t检验结果说明，新算法具有明显的优势。

关键词: 粒子群优化算法, 全局搜索, 局部搜索, 快速收敛, 自适应逃逸

CLC Number:

TP301.6

SHI Xiaolu SUN Hui LI Jun ZHU Degang. Particle swarm optimization algorithm with fast convergence and adaptive escape[J]. Journal of Computer Applications, 2013, 33(05): 1308-1312.

史小露孙辉李俊朱德刚. 具有快速收敛和自适应逃逸功能的粒子群优化算法[J]. 计算机应用, 2013, 33(05): 1308-1312.

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Particle swarm optimization algorithm with fast convergence and adaptive escape

具有快速收敛和自适应逃逸功能的粒子群优化算法

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