Genetic learning particle swarm optimization

Gong, Y.-J., Li, J.-J., Zhou, Y., Li, Y. , Chung, H. S.-H., Shi, Y.-H. and Zhang, J. (2016) Genetic learning particle swarm optimization. IEEE Transactions on Cybernetics, 46(10), pp. 2277-2290. (doi:10.1109/TCYB.2015.2475174) (PMID:26394440)

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Abstract

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

Item Type:Articles
Additional Information:This work was supported in part by the National High-Technology Research and Development Program (863 Program) of China under Grant 2013AA01A212, in part by the National Science Fund for Distinguished Young Scholars under Grant 61125205, and in part by the National Natural Science Foundation of China under Grant 6120002 and Grant 61502542.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Professor Yun
Authors: Gong, Y.-J., Li, J.-J., Zhou, Y., Li, Y., Chung, H. S.-H., Shi, Y.-H., and Zhang, J.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Cybernetics
Journal Abbr.:IEEE Trans Cybern
Publisher:IEEE
ISSN:2168-2267
Published Online:17 September 2015
Copyright Holders:Copyright © 2015 IEEE
First Published:First published in IEEE Transactions on Cybernetics 46(10): 2277-2290
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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