Quantum-Behaved Particle Swarm Optimization with Chaotic Search
Summary :
The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.7 pp.1963-1970
- Publication Date
- 2008/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.7.1963
- Type of Manuscript
- PAPER
- Category
- Algorithm Theory
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Keyword
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Kaiqiao YANG, Hirosato NOMURA, "Quantum-Behaved Particle Swarm Optimization with Chaotic Search" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 1963-1970, July 2008, doi: 10.1093/ietisy/e91-d.7.1963.
Abstract: The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.1963/_p
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@ARTICLE{e91-d_7_1963,
author={Kaiqiao YANG, Hirosato NOMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Quantum-Behaved Particle Swarm Optimization with Chaotic Search},
year={2008},
volume={E91-D},
number={7},
pages={1963-1970},
abstract={The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.},
keywords={},
doi={10.1093/ietisy/e91-d.7.1963},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Quantum-Behaved Particle Swarm Optimization with Chaotic Search
T2 - IEICE TRANSACTIONS on Information
SP - 1963
EP - 1970
AU - Kaiqiao YANG
AU - Hirosato NOMURA
PY - 2008
DO - 10.1093/ietisy/e91-d.7.1963
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.
ER -
English
