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Particle Swarm Optimization with Gravitational Interactions for Multimodal and Unimodal Problems

  • Juan J. Flores
  • Rodrigo López
  • Julio Barrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)

Abstract

Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this paper we show a population-based metaheuristic inspired on the gravitational forces produced by the interaction of the masses of a set of bodies. We explored the physics knowledge in order to find useful analogies to design an optimization metaheuristic. The proposed algorithm is capable to find the optima of unimodal and multimodal functions commonly used to benchmark evolutionary algorithms. We show that the proposed algorithm works and outperforms PSO with niches in both cases. Our algorithm does not depend on a radius parameter and does not need to use niches to solve multimodal problems. We compare with other metaheuristics respect to the mean number of evaluations needed to find the optima.

Keywords

Optimization gravitational interactions evolutionary computation metaheuristic 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan J. Flores
    • 1
  • Rodrigo López
    • 1
  • Julio Barrera
    • 2
  1. 1.División de Estudios de Posgrado, Facultad de Ingeniería EléctricaUniversidad Michoacana de San Nicolás de HidalgoMexico
  2. 2.CINVESTAV-IPN, Departamento de ComputaciónEvolutionary Computation GroupMéxico, D.F.México

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