Metropolis Particle Swarm Optimization Algorithm with Mutation Operator For Global Optimization Problems

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

inria-00524976, version 1

Metropolis Particle Swarm Optimization Algorithm with Mutation Operator For Global Optimization Problems

Lhassane Idoumghar () ¹, Maha Idrissi Aouad ( Auteur à contacter de préférence ) ², Mahmoud Melkemi () ¹, René Schott () ^3, 4

IEEE-ICTAI 2010 (22th International Conference on Tools with Articial Intelligence) (2010) 35-42

Résumé : When a local optimal solution is reached with classical Particle Swarm Optimization (PSO), all particles in the swarm gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present in this paper a novel variant of PSO algorithm, called MPSOM, that uses Metropolis equation to update local best solutions (lbest) of each particle and uses mutation operator to escape from local optima. The proposed MPSOM algorithm is validated on seven standard benchmark functions and used to solve the problem of reducing memory energy consumption in embedded systems (Scratch-Pad Memories SPMs). The numerical results show that our approach outperforms several recently published algorithms.

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

inria-00524976, version 1
http://hal.inria.fr/inria-00524976
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Contributeur : Maha Idrissi Aouad <>
Soumis le : Dimanche 10 Octobre 2010, 01:48:05
Dernière modification le : Mardi 2 Novembre 2010, 16:02:03

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%F inria-00524976, version 1
%0 Conference Proceedings
%U http://hal.inria.fr/inria-00524976
%2 INFO:INFO_AI;INFO:INFO_PL;MATH:MATH_OC
%T Metropolis Particle Swarm Optimization Algorithm with Mutation Operator For Global Optimization Problems
%A Idoumghar, Lhassane
%A Idrissi Aouad, Maha
%A Melkemi, Mahmoud
%A Schott, René
%+ Laboratoire de Mathématiques Informatique et Applications - LMIA , TRIO - INRIA Lorraine - LORIA , Laboratoire Lorrain de Recherche en Informatique et ses Applications - LORIA , Institut Elie Cartan Nancy - IECN
%X When a local optimal solution is reached with classical Particle Swarm Optimization (PSO), all particles in the swarm gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present in this paper a novel variant of PSO algorithm, called MPSOM, that uses Metropolis equation to update local best solutions (lbest) of each particle and uses mutation operator to escape from local optima. The proposed MPSOM algorithm is validated on seven standard benchmark functions and used to solve the problem of reducing memory energy consumption in embedded systems (Scratch-Pad Memories SPMs). The numerical results show that our approach outperforms several recently published algorithms.
%2 G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS/G.2.1: Combinatorics/G.2.1.0: Combinatorial algorithms, I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.8: Problem Solving, Control Methods, and Search/I.2.8.3: Graph and tree search strategies, D.: Software/D.3: PROGRAMMING LANGUAGES/D.3.4: Processors/D.3.4.5: Memory management (garbage collection), I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.8: Problem Solving, Control Methods, and Search/I.2.8.4: Heuristic methods, D.: Software/D.2: SOFTWARE ENGINEERING/D.2.8: Metrics/D.2.8.1: Performance measures, D.: Software/D.1: PROGRAMMING TECHNIQUES/D.1.0: General
%G Anglais
%8 2010-10-00
%2 internationale
%B IEEE-ICTAI 2010 (22th International Conference on Tools with Articial Intelligence)
%2 Arras
%2 France
%8 2010-10-27
%8 2010-10-29
%P 35-42

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