An Improved Central Force Optimization Algorithm for Multimodal Optimization
This paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization. CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical resu...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/895629 |
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| _version_ | 1849415080739864576 |
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| author | Jie Liu Yu-ping Wang |
| author_facet | Jie Liu Yu-ping Wang |
| author_sort | Jie Liu |
| collection | DOAJ |
| description | This paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization. CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical results can be omitted, and the convergence intends to produce faster and more accurate by clustering technique and good points set. When tested against benchmark functions, in low and high dimensions, the CSM-CFO algorithm has competitive performance in terms of accuracy and convergence speed compared to other evolutionary algorithms: particle swarm optimization, evolutionary program, and simulated annealing. The comparison results demonstrate that the proposed algorithm is effective and efficient. |
| format | Article |
| id | doaj-art-581b0e68a69b451cb2c1477dff046dce |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-581b0e68a69b451cb2c1477dff046dce2025-08-20T03:33:38ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/895629895629An Improved Central Force Optimization Algorithm for Multimodal OptimizationJie Liu0Yu-ping Wang1School of Mathematics and Statistics, Xi’dian University, Xi’an 710071, ChinaSchool of Computer, Xi’dian University, Xi’an 710071, ChinaThis paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization. CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical results can be omitted, and the convergence intends to produce faster and more accurate by clustering technique and good points set. When tested against benchmark functions, in low and high dimensions, the CSM-CFO algorithm has competitive performance in terms of accuracy and convergence speed compared to other evolutionary algorithms: particle swarm optimization, evolutionary program, and simulated annealing. The comparison results demonstrate that the proposed algorithm is effective and efficient.http://dx.doi.org/10.1155/2014/895629 |
| spellingShingle | Jie Liu Yu-ping Wang An Improved Central Force Optimization Algorithm for Multimodal Optimization Journal of Applied Mathematics |
| title | An Improved Central Force Optimization Algorithm for Multimodal Optimization |
| title_full | An Improved Central Force Optimization Algorithm for Multimodal Optimization |
| title_fullStr | An Improved Central Force Optimization Algorithm for Multimodal Optimization |
| title_full_unstemmed | An Improved Central Force Optimization Algorithm for Multimodal Optimization |
| title_short | An Improved Central Force Optimization Algorithm for Multimodal Optimization |
| title_sort | improved central force optimization algorithm for multimodal optimization |
| url | http://dx.doi.org/10.1155/2014/895629 |
| work_keys_str_mv | AT jieliu animprovedcentralforceoptimizationalgorithmformultimodaloptimization AT yupingwang animprovedcentralforceoptimizationalgorithmformultimodaloptimization AT jieliu improvedcentralforceoptimizationalgorithmformultimodaloptimization AT yupingwang improvedcentralforceoptimizationalgorithmformultimodaloptimization |