A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm
We compare 27 modifications of the original particle swarm optimization (PSO) algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/293087 |
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| author | Michala Jakubcová Petr Máca Pavel Pech |
| author_facet | Michala Jakubcová Petr Máca Pavel Pech |
| author_sort | Michala Jakubcová |
| collection | DOAJ |
| description | We compare 27 modifications of the original particle swarm optimization (PSO) algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO), the second strategy periodically partitions the population into equally large complexes according to the particle’s functional value (SCE-PSO), and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO). All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process. |
| format | Article |
| id | doaj-art-8e15a0f310aa44569666e67c6d2a630f |
| 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-8e15a0f310aa44569666e67c6d2a630f2025-02-03T05:46:41ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/293087293087A Comparison of Selected Modifications of the Particle Swarm Optimization AlgorithmMichala Jakubcová0Petr Máca1Pavel Pech2Department of Water Resources and Environmental Modeling, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 1176, Prague 6, 165 21 Suchdol, Czech RepublicDepartment of Water Resources and Environmental Modeling, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 1176, Prague 6, 165 21 Suchdol, Czech RepublicDepartment of Water Resources and Environmental Modeling, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 1176, Prague 6, 165 21 Suchdol, Czech RepublicWe compare 27 modifications of the original particle swarm optimization (PSO) algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO), the second strategy periodically partitions the population into equally large complexes according to the particle’s functional value (SCE-PSO), and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO). All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process.http://dx.doi.org/10.1155/2014/293087 |
| spellingShingle | Michala Jakubcová Petr Máca Pavel Pech A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm Journal of Applied Mathematics |
| title | A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm |
| title_full | A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm |
| title_fullStr | A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm |
| title_full_unstemmed | A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm |
| title_short | A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm |
| title_sort | comparison of selected modifications of the particle swarm optimization algorithm |
| url | http://dx.doi.org/10.1155/2014/293087 |
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