doi: 10.4304/jsw.9.2.350-257
Particle Swarm Optimization Algorithm with Reverse-Learning and Local-Learning Behavior
- 1 National Engineering Research Center for Satellite Positioning System, Wuhan University, China Hubei Engineering University, Xiaogan, China
- 2 School of Computer, Wuhan University, Hubei Wuhan 430079, China
Abstract
In order to resolve conflict between convergencespeed and population diversity of particle swarmoptimization (PSO) algorithm, an improved PSO, calledreverse-learning and local-learning PSO (RLPSO)algorithm, is presented in which a reverse-learning behaviorimplemented by some particles while local-learningbehavior adopted by elite particles in each generation.During the reverse-learning process, some inferior particlesof initial population and each particle’s historical worstposition are reserved to attract a particle to leap out of localoptimums. Furthermore, the Hamming distance betweenthe inferior particles is set to no less than a default rejectiondistance the aim of which is to maintain the diversity ofpopulation and improve RLPSO’s exploration ability. Ineach generation, the difference between the best particleand the second-best particle is used to guide the best one tocarry out local search which is crucial for improvingRLPSO’s exploitation ability. The results of experimentsshow that RLPSO has a good global searching ability andconvergence speed especially in high dimension function.
Keywords
- particle swarm optimization
- reverselear
How to Cite
Xuewen Xia, Jingnan Liu, and Yuanxiang Li, "Particle Swarm Optimization Algorithm with Reverse-Learning and Local-Learning Behavior," Journal of Software, vol. 9, no. 2, pp. 350-357, 2014. https://doi.org/ 10.4304/jsw.9.2.350-257
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).