Genetic Learning Particle Swarm Optimization

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 mechanis...

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Vydané v:IEEE transactions on cybernetics Ročník 46; číslo 10; s. 2277 - 2290
Hlavní autori: Gong, Yue-Jiao, Li, Jing-Jing, Zhou, Yicong, Li, Yun, Chung, Henry Shu-Hung, Shi, Yu-Hui, Zhang, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.10.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Algorithms
Animals
Artificial Intelligence
Bees
Computer Simulation
Convergence
Efficiency
Evolution & development
Exemplar construction
genetic algorithm (GA)
Genetic algorithms
Genetics
Genetics, Behavioral
hybrid method
learning scheme
Models, Genetic
Optimization techniques
particle swarm optimization (PSO)
Sociology
Statistics
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí: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.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2015.2475174