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Genetic Learning Particle Swarm Optimization
Gong Y.-J.1,2,3; Li J.-J.4; Zhou Y.5; Li Y.6; Chung H.S.-H.7; Shi Y.-H.8; Zhang J.1,2,3
Source PublicationIEEE Transactions on Cybernetics

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. © 2015 IEEE.

KeywordExemplar Construction Genetic Algorithm (Ga) Hybrid Method Learning Scheme Particle Swarm Optimization (Pso)
URLView the original
Indexed BySCI
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000384265600007
The Source to ArticleScopus
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Cited Times [WOS]:120   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorZhang J.
Affiliation1.Department of Computer Science, Sun Yat-sen University, Guangzhou 510275, China
2.Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China
3.Engineering Research Center of Supercomputing Engineering Software, Ministry of Education, Guangzhou 510006, China
4.School of Computer Science, South China Normal University, Guangzhou 510006, China
5.Department of Computer and Information Science, University of Macau, Macau 999078, China
6.School of Engineering, University of Glasgow, Glasgow G12 8QQ, U.K.
7.Department of Electronic Engineering, City University of Hong Kong, Hong Kong
8.Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China.
Recommended Citation
GB/T 7714
Gong Y.-J.,Li J.-J.,Zhou Y.,et al. Genetic Learning Particle Swarm Optimization[J]. IEEE Transactions on Cybernetics,2016,46(10):2277.
APA Gong Y.-J..,Li J.-J..,Zhou Y..,Li Y..,Chung H.S.-H..,...&Zhang J..(2016).Genetic Learning Particle Swarm Optimization.IEEE Transactions on Cybernetics,46(10),2277.
MLA Gong Y.-J.,et al."Genetic Learning Particle Swarm Optimization".IEEE Transactions on Cybernetics 46.10(2016):2277.
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