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

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.
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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 : jeudi 11 janvier 2018 - 06:25:24

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  • HAL Id : inria-00524976, version 1

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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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