A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy
Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledg...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/713490 |
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| author | Guohua Wu Witold Pedrycz Manhao Ma Dishan Qiu Haifeng Li Jin Liu |
| author_facet | Guohua Wu Witold Pedrycz Manhao Ma Dishan Qiu Haifeng Li Jin Liu |
| author_sort | Guohua Wu |
| collection | DOAJ |
| description | Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge. |
| format | Article |
| id | doaj-art-1be8ffa37d9e49ee8c3fab7309163c8a |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-1be8ffa37d9e49ee8c3fab7309163c8a2025-02-03T01:10:28ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/713490713490A Particle Swarm Optimization Variant with an Inner Variable Learning StrategyGuohua Wu0Witold Pedrycz1Manhao Ma2Dishan Qiu3Haifeng Li4Jin Liu5Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaDepartment of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, CanadaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaSchool of Civil Engineering and Architecture, Central South University, Changsha , Hunan 410004, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaAlthough Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.http://dx.doi.org/10.1155/2014/713490 |
| spellingShingle | Guohua Wu Witold Pedrycz Manhao Ma Dishan Qiu Haifeng Li Jin Liu A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy The Scientific World Journal |
| title | A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy |
| title_full | A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy |
| title_fullStr | A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy |
| title_full_unstemmed | A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy |
| title_short | A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy |
| title_sort | particle swarm optimization variant with an inner variable learning strategy |
| url | http://dx.doi.org/10.1155/2014/713490 |
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