Adaptive parameter selection scheme for PSO: A learning automata approach

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting. As modifying a single parameter may result in a large effect. In this paper, we propose a new a new learning automata-based approach for adaptive PSO parameter selection. In this approach three learning autom...

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Bibliographic Details
Published in2009 14th International CSI Computer Conference pp. 403 - 411
Main Authors Hashemi, A.B., Meybodi, M.R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Acceleration
Decision making
Evolutionary computation
Genetic mutations
Information technology
Learning automata
Particle swarm optimization
Random variables
Search methods
Stochastic processes
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ISBN9781424442614
1424442613
DOI10.1109/CSICC.2009.5349614

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Summary:PSO, like many stochastic search methods, is very sensitive to efficient parameter setting. As modifying a single parameter may result in a large effect. In this paper, we propose a new a new learning automata-based approach for adaptive PSO parameter selection. In this approach three learning automata are utilized to determine values of each parameter for updating particles velocity namely inertia weight, cognitive and social components. Experimental results show that the proposed algorithms compared to other schemes such as SPSO, PSO-IW, PSO TVAC, PSO-LP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better local minima. In addition, proposed algorithms converge to stopping criteria significantly faster than most of the PSO algorithms.
ISBN:9781424442614
1424442613
DOI:10.1109/CSICC.2009.5349614