Presentation 2011-01-25
A Proposal of Evolutionary Particle Swarm Optimizer with Inertia Weight
Hong ZHANG,
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Abstract(in English) In this paper, we propose an evolutionary particle swarm optimizer with inertia weight (EPSOIW) for obtaining the PSOIW with high-performance. Due to the use of meta-optimization, it can efficiently find appropriate values of parameters in the PSOIW to a given optimization problem. Accordingly, the EPSOIW could be expected to not only obtain an optimal PSOIW without prior knowledge, but also to quantitatively analyze the know-how on designing it. To demonstrate the effectiveness of the proposal, computer experiments on a suite of multidimensional benchmark problems are carried out. The obtained experimental results show that the EPSOIW has remarkable search performance in comparison with the original PSOIW and other methods.
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Keyword(in English) evolutionary particle swarm optimization / meta-optimization / parameter selection / a real-coded genetic algorithm / a temporally cumulative fitness function
Paper # NLP2010-151,NC2010-115
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Committee NC
Conference Date 2011/1/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Proposal of Evolutionary Particle Swarm Optimizer with Inertia Weight
Sub Title (in English)
Keyword(1) evolutionary particle swarm optimization
Keyword(2) meta-optimization
Keyword(3) parameter selection
Keyword(4) a real-coded genetic algorithm
Keyword(5) a temporally cumulative fitness function
1st Author's Name Hong ZHANG
1st Author's Affiliation Graduate School of Life Science & Systems Engineering, Kyushu Institute of Technology()
Date 2011-01-25
Paper # NLP2010-151,NC2010-115
Volume (vol) vol.110
Number (no) 388
Page pp.pp.-
#Pages 6
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