Genetic learning particle swarm optimization
Gong, Yue-Jiao and Li, Jing-Jing and Zhou, Yicong and Li, Yun and Chung, Henry Shu-Hung and Shi, Yu-Hui and Zhang, Jun (2016) Genetic learning particle swarm optimization. IEEE Transactions on Cybernetics, 46 (10). pp. 2277-2290. 7271066. ISSN 2168-2275 (https://doi.org/10.1109/TCYB.2015.2475174)
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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.
ORCID iDs
Gong, Yue-Jiao, Li, Jing-Jing, Zhou, Yicong, Li, Yun ORCID: https://orcid.org/0000-0002-6575-1839, Chung, Henry Shu-Hung, Shi, Yu-Hui and Zhang, Jun ORCID: https://orcid.org/0000-0002-3731-4594;-
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Item type: Article ID code: 65132 Dates: DateEvent31 October 2016Published17 September 2015Published Online21 August 2015AcceptedNotes: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Engineering
Faculty of Engineering > Mechanical and Aerospace EngineeringDepositing user: Pure Administrator Date deposited: 13 Aug 2018 09:17 Last modified: 26 Nov 2024 02:54 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/65132