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

Lhassane Idoumghar 1 Maha Idrissi Aouad 2, * Mahmoud Melkemi 1 René Schott 3, 4
* Auteur correspondant
1 LMIA - Laboratoire de Mathématiques Informatique et Applications
2 TRIO - Real time and interoperability
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
4 IECN - Institut Élie Cartan de Nancy
Abstract : 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.
Type de document :
Communication dans un congrès
IEEE-ICTAI 2010 (22th International Conference on Tools with Articial Intelligence), Oct 2010, Arras, France. pp.35-42, 2010
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https://hal.inria.fr/inria-00524976
Contributeur : Maha Idrissi Aouad <>
Soumis le : dimanche 10 octobre 2010 - 01:48:05
Dernière modification le : mardi 16 octobre 2018 - 14:26:02

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Lhassane Idoumghar, Maha Idrissi Aouad, Mahmoud Melkemi, René Schott. Metropolis Particle Swarm Optimization Algorithm with Mutation Operator For Global Optimization Problems. IEEE-ICTAI 2010 (22th International Conference on Tools with Articial Intelligence), Oct 2010, Arras, France. pp.35-42, 2010. 〈inria-00524976〉

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