Modified binary particle swarm optimization
This paper presents a modified binary particle swarm optimization （BPSO） which adopts concepts of the genotype-phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on vari...
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| Published in | Progress in natural science Vol. 18; no. 9; pp. 1161 - 1166 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.09.2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-0071 |
| DOI | 10.1016/j.pnsc.2008.03.018 |
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| Abstract | This paper presents a modified binary particle swarm optimization （BPSO） which adopts concepts of the genotype-phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. Experimental results show that the modified BPSO outperforms the original BPSO algorithm. |
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| AbstractList | This paper presents a modified binary particle swarm optimization （BPSO） which adopts concepts of the genotype-phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. Experimental results show that the modified BPSO outperforms the original BPSO algorithm. This paper presents a modified binary particle swarm optimization (BPSO) which adopts concepts of the genotype–phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. Experimental results show that the modified BPSO outperforms the original BPSO algorithm. |
| Author | Sangwook Lee Sangmoon Soak Sanghoun Oh Witold Pedryc Moongu Jeon |
| AuthorAffiliation | College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA Information Systems Examination Team, Korean Intellectual Property Office （K1PO）, Government Complex Daejeon Building 4, 920 Dunsandong, Seogu, Republic of Korea Department oflnformation and Communications, Gwangju Institute of Science and Technology, 261 Cheomdan-gwagiro, Buk-gu, Gwanju, Republic of Korea Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada, T6G 2II4 |
| Author_xml | – sequence: 1 givenname: Sangwook surname: Lee fullname: Lee, Sangwook organization: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA – sequence: 2 givenname: Sangmoon surname: Soak fullname: Soak, Sangmoon organization: Information Systems Examination Team, Korean Intellectual Property Office (KIPO), Government Complex Daejeon Building 4, 920 Dunsandong, Seogu, Republic of Korea – sequence: 3 givenname: Sanghoun surname: Oh fullname: Oh, Sanghoun organization: Department of Information and Communications, Gwangju Institute of Science and Technology, 261 Cheomdan-gwagiro, Buk-gu, Gwanju, Republic of Korea – sequence: 4 givenname: Witold surname: Pedrycz fullname: Pedrycz, Witold organization: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada, T6G 2V4 – sequence: 5 givenname: Moongu surname: Jeon fullname: Jeon, Moongu email: mgjeon@gist.ac.kr organization: Department of Information and Communications, Gwangju Institute of Science and Technology, 261 Cheomdan-gwagiro, Buk-gu, Gwanju, Republic of Korea |
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| Cites_doi | 10.1109/4235.985692 10.1093/ietfec/e90-a.10.2253 10.1287/ijoc.6.2.154 10.1109/CEC.2002.1004493 |
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| Keywords | Genotype–phenotype Binary particle swarm optimization Mutation |
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| Notes | Binary particle swarm optimization Binary particle swarm optimization; Genotype-phenotype; Mutation Mutation Q343.1 Genotype-phenotype 11-3853/N |
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