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Gravitational Interactions Optimization

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6683)

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 (Gravitational Interactions Optimization - GIO) 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 GIO with other metaheuristics with respect to the mean number of evaluations needed to find the optima.

Keywords

  • Optimization
  • gravitational interactions
  • evolutionary computation
  • metaheuristic

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  • DOI: 10.1007/978-3-642-25566-3_17
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© 2011 Springer-Verlag Berlin Heidelberg

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Flores, J.J., López, R., Barrera, J. (2011). Gravitational Interactions Optimization. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_17

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  • DOI: https://doi.org/10.1007/978-3-642-25566-3_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25565-6

  • Online ISBN: 978-3-642-25566-3

  • eBook Packages: Computer ScienceComputer Science (R0)