Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

Lhassane Idoumghar; Mahmoud Melkemi; René Schott; Maha Idrissi Aouad

hal-00601360, version 1

Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

Lhassane Idoumghar () ¹, Mahmoud Melkemi () ¹, René Schott () ^2, 3, Maha Idrissi Aouad () ⁴

Applied Computational Intelligence and Soft Computing 2011 (2011) Article ID 138078

Abstract: 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.

1: Laboratoire de Mathématiques Informatique et Applications (LMIA)
Université de Haute Alsace - Mulhouse
2: Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
3: Institut Elie Cartan Nancy (IECN)
CNRS : UMR7502 – INRIA – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
4: TRIO (INRIA Lorraine - LORIA)
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)

Collaboration : TRIO

hal-00601360, version 1
http://hal.archives-ouvertes.fr/hal-00601360
oai:hal.archives-ouvertes.fr:hal-00601360
From: Maha Idrissi Aouad <>
Submitted on: Friday, 17 June 2011 14:27:31
Updated on: Monday, 19 March 2012 10:10:08

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DOI: 10.1155/2011/138078

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%% hal-00601360, version 1
%% http://hal.archives-ouvertes.fr/hal-00601360
@article{idoumghar:hal-00601360,
    hal_id = {hal-00601360},
    url = {http://hal.archives-ouvertes.fr/hal-00601360},
    title = {{Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems}},
    author = {Idoumghar, Lhassane and Melkemi, Mahmoud and Schott, Ren{\'e} and Idrissi Aouad, Maha},
    abstract = {{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.}},
    language = {English},
    affiliation = {Laboratoire de Math{\'e}matiques Informatique et Applications - LMIA , Laboratoire Lorrain de Recherche en Informatique et ses Applications - LORIA , Institut Elie Cartan Nancy - IECN , TRIO - INRIA Lorraine - LORIA},
    pages = {Article ID 138078},
    journal = {Applied Computational Intelligence and Soft Computing},
    volume = {2011},
    note = {12 pages },
    audience = {international },
    collaboration = {TRIO },
    doi = {10.1155/2011/138078 },
    year = {2011},
    month = Jun,
    pdf = {http://hal.archives-ouvertes.fr/hal-00601360/PDF/138078.pdf},
}

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