Parallel Swarms Oriented Particle Swarm Optimization
The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal fun...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2013/756719 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562699015290880 |
|---|---|
| author | Tad Gonsalves Akira Egashira |
| author_facet | Tad Gonsalves Akira Egashira |
| author_sort | Tad Gonsalves |
| collection | DOAJ |
| description | The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO. |
| format | Article |
| id | doaj-art-ca7c97e0ecf64e57877f1a2581e49e1b |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-ca7c97e0ecf64e57877f1a2581e49e1b2025-02-03T01:22:08ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322013-01-01201310.1155/2013/756719756719Parallel Swarms Oriented Particle Swarm OptimizationTad Gonsalves0Akira Egashira1Department of Information and Communication Sciences, Faculty of Science & Technology, Sophia University, 7-1 Kioicho, Chiyoda-ku, Tokyo 102-8554, JapanDepartment of Information and Communication Sciences, Faculty of Science & Technology, Sophia University, 7-1 Kioicho, Chiyoda-ku, Tokyo 102-8554, JapanThe particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO.http://dx.doi.org/10.1155/2013/756719 |
| spellingShingle | Tad Gonsalves Akira Egashira Parallel Swarms Oriented Particle Swarm Optimization Applied Computational Intelligence and Soft Computing |
| title | Parallel Swarms Oriented Particle Swarm Optimization |
| title_full | Parallel Swarms Oriented Particle Swarm Optimization |
| title_fullStr | Parallel Swarms Oriented Particle Swarm Optimization |
| title_full_unstemmed | Parallel Swarms Oriented Particle Swarm Optimization |
| title_short | Parallel Swarms Oriented Particle Swarm Optimization |
| title_sort | parallel swarms oriented particle swarm optimization |
| url | http://dx.doi.org/10.1155/2013/756719 |
| work_keys_str_mv | AT tadgonsalves parallelswarmsorientedparticleswarmoptimization AT akiraegashira parallelswarmsorientedparticleswarmoptimization |