Presentation | 2011-01-25 A Proposal of Evolutionary Particle Swarm Optimizer with Inertia Weight Hong ZHANG, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
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 |
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Conference Date | 2011/1/17(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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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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