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Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior

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Abstract

In this paper, a strategy for multi-objective optimization based upon the behavior of a particle swarm with rotational and linear motion is presented. The strategy for multi-objective optimization is based upon the emulation of the linear and circular movements of a swarm (flock). Thus emerges the physical basis for the cognitive model, which in conjunction with exploration–exploitation results in the proposal of a cognitive algorithm, which is tested through several multi-objective optimization functions. The algorithm proposed is compared with standard particle swarm optimization multi-objective via statistical analysis.

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Authors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Carrera 8 No 40-62, Bogotá, Colombia

    Joaquín Meza, Helbert Espitia & Carlos Montenegro

  2. Universidad Internacional de La Rioja (UNIR), Rectorado-Gran Vía Rey Juan Carlos I, 41, Logroño, La Rioja, Spain

    Rubén González Crespo

Authors
  1. Joaquín Meza
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  2. Helbert Espitia
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  3. Carlos Montenegro
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  4. Rubén González Crespo
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Correspondence to Rubén González Crespo.

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Communicated by A. Di Nola.

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Meza, J., Espitia, H., Montenegro, C. et al. Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior. Soft Comput 20, 3521–3536 (2016). https://doi.org/10.1007/s00500-015-1972-2

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