Improved particle swarm optimization algorithm using mean information and elitist mutation

doi:10.11772/j.issn.1001-9081.2014.11.3241

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3241-3244.DOI: 10.11772/j.issn.1001-9081.2014.11.3241

• Artificial intelligence • Previous Articles Next Articles

Improved particle swarm optimization algorithm using mean information and elitist mutation

LIN Guohan¹,²,ZHANG Jing²,LIU Zhaohua⁴

Received:2014-05-14 Revised:2014-06-30 Online:2014-12-01 Published:2014-11-01
Contact: LIN Guohan
Supported by:
;China Postdoctoral Science Foundation

采用种群平均信息和精英变异的改进粒子群算法

林国汉¹,²,章兢³,⁴,刘朝华⁵

1. 湖南大学电气与信息工程学院,长沙 410082;
2. 湖南工程学院电气信息学院,湖南湘潭 411101;
3. 湖南大学电气与信息工程学院
4. 湖南大学电气与信息工程学院,长沙 410082
5. 湖南科技大学信息与电气工程学院,湖南湘潭 411021

通讯作者: 林国汉
作者简介:林国汉(1973-),男,广东高州人,博士研究生,主要研究方向: 智能计算、复杂系统计算机控制;章兢(1957-),男,湖南湘潭人,教授,博士生导师,主要研究方向: 复杂工业系统优化控制、智能优化、并行计算、云计算;刘朝华(1983-),男,湖南娄底人,博士,主要研究方向:复杂系统、并行计算、云计算。
基金资助:
江苏高校优势学科建设工程项目;中国博士后科学基金资助项目;湖南省自然科学基金资助项目

Abstract

Abstract:

Concerning that conventional Particle Swarm Optimization (PSO) is easy trapped in local optima and with low search efficiency in later stage, an improved PSO based on mean information and elitist mutation, named MEPSO, was proposed. Average information of swarm was introduced into MEPSO to improve the global search ability, and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. In the latter stage of the iteration, the Cauchy mutation operation was applied to the global best particle to improve the global search ability and to further reduce the risk of trapping into local optimum. Contrast experiments on six benchmark functions were given. Compared with Basic PSO (BPSO), PSO with TVAC (PSO-TVAC), PSO with Time-Varying Inertia Weight factor (PSO-TVIW) and Hybrid PSO with Wavelet Mutation (HPSOWM), MEPSO achieved better mean value and standard variance with shorter optimization time and better reliability. The results show that MEPSO can better balance the ability of local search and global search, and can converge faster with higher accuracy and efficiency.

摘要：

针对基本粒子群优化(PSO)算法早熟收敛和后期搜索效率低的问题,提出一种利用种群平均信息和精英变异的粒子群优化算法——MEPSO算法。该算法引入粒子个体与群体的平均信息,利用粒子平均信息来提高算法全局搜索能力,并采用时变加速系数(TVAC)以平衡算法的局部搜索和全局搜索能力;在算法后期,采用精英学习策略对精英粒子进行柯西变异操作,以进一步提高算法的全局搜索能力,减少算法陷入局部最优的危险。在6个典型的复杂函数上与基本PSO(BPSO)算法、时变加速因子PSO(PSO-TVAC)算法、时变惯性权重PSO(PSO-TVIW)算法和小波变异PSO(HPSOWM)算法进行对比,MEPSO的均值与标准方差均优于对比算法,且寻优时间最短,可靠性更好。结果表明, MEPSO能较好地兼顾局部搜索和全局搜索能力,收敛速度快,收敛精度和搜索效率高。

CLC Number:

TP301.6

LIN Guohan ZHANG Jing LIU Zhaohua. Improved particle swarm optimization algorithm using mean information and elitist mutation[J]. Journal of Computer Applications, 2014, 34(11): 3241-3244.

林国汉章兢刘朝华. 采用种群平均信息和精英变异的改进粒子群算法[J]. 计算机应用, 2014, 34(11): 3241-3244.

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Improved particle swarm optimization algorithm using mean information and elitist mutation

采用种群平均信息和精英变异的改进粒子群算法

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