Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems
The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid prematu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2011/138078 |
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| _version_ | 1850157171159859200 |
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| author | Lhassane Idoumghar Mahmoud Melkemi René Schott Maha Idrissi Aouad |
| author_facet | Lhassane Idoumghar Mahmoud Melkemi René Schott Maha Idrissi Aouad |
| author_sort | Lhassane Idoumghar |
| collection | DOAJ |
| description | The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms.
When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs. |
| format | Article |
| id | doaj-art-33f012eaf60743aa92fea9548f182bc7 |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-33f012eaf60743aa92fea9548f182bc72025-08-20T02:24:15ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322011-01-01201110.1155/2011/138078138078Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded SystemsLhassane Idoumghar0Mahmoud Melkemi1René Schott2Maha Idrissi Aouad3INRIA Nancy—Grand Est/LORIA, 615 Rue du Jardin Botanique, 54600 Villers-Lès-Nancy, FranceLMIA—MAGE, Université de Haute-Alsace, 4 Rue des Frères Lumière, 68093 Mulhouse, FranceIECN—LORIA, Nancy-Université, Université Henri Poincaré, 54506 Vandoeuvre-Lès-Nancy, FranceINRIA Nancy—Grand Est/LORIA, 615 Rue du Jardin Botanique, 54600 Villers-Lès-Nancy, FranceThe paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.http://dx.doi.org/10.1155/2011/138078 |
| spellingShingle | Lhassane Idoumghar Mahmoud Melkemi René Schott Maha Idrissi Aouad Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems Applied Computational Intelligence and Soft Computing |
| title | Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems |
| title_full | Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems |
| title_fullStr | Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems |
| title_full_unstemmed | Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems |
| title_short | Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems |
| title_sort | hybrid pso sa type algorithms for multimodal function optimization and reducing energy consumption in embedded systems |
| url | http://dx.doi.org/10.1155/2011/138078 |
| work_keys_str_mv | AT lhassaneidoumghar hybridpsosatypealgorithmsformultimodalfunctionoptimizationandreducingenergyconsumptioninembeddedsystems AT mahmoudmelkemi hybridpsosatypealgorithmsformultimodalfunctionoptimizationandreducingenergyconsumptioninembeddedsystems AT reneschott hybridpsosatypealgorithmsformultimodalfunctionoptimizationandreducingenergyconsumptioninembeddedsystems AT mahaidrissiaouad hybridpsosatypealgorithmsformultimodalfunctionoptimizationandreducingenergyconsumptioninembeddedsystems |