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Hybrid PSO6 for hard continuous optimization

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Abstract

In our previous works, we empirically showed that a number of \(6_{\pm 2}\) informants may endow particle swarm optimization (PSO) with an optimized learning procedure in comparison with other combinations of informants. In this way, the new version PSO6, that evolves new particles from six informants (neighbors), performs more accurately than other existing versions of PSO and is able to generate good particles for a longer time. Despite this advantage, PSO6 may show certain attraction to local basins derived from its moderate performance on non-separable complex problems (typically observed in PSO versions). In this paper, we incorporate a local search procedure to the PSO6 with the aim of correcting this disadvantage. We compare the performance of our proposal (PSO6-Mtsls) on a set of 40 benchmark functions against that of other PSO versions, as well as against the best recent proposals in the current state of the art (with and without local search). The results support our conjecture that the (quasi)-optimally informed PSO, hybridized with local search mechanisms, reaches a high rate of success on a large number of complex (non-separable) continuous optimization functions.

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Notes

  1. MALLBA Library, Directory Mallba/rep/PSO/soco2010

    http://neo.lcc.uma.es/mallba/easy-mallba/html/mallba.html.

  2. The complete information about featured algorithms in SOCO’10 is available in http://sci2s.ugr.es/EAMHCO/.

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Acknowledgments

Authors acknowledge funds from the Spanish Ministry of Economy and Competitiveness (MEC) and FEDER under contract TIN2011-28194 (RoadMe project http://roadme.lcc.uma.es). It is also partially founded by project number 8.06/5.47.4142 in collaboration with the VSB-Technical University of Ostrava.

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  1. Department of Lenguajes y Ciencias de la Computación, E.T.S.I. Informática, University of Málaga, Campus de Teatinos, 29071 , Malaga, Spain

    José García-Nieto & Enrique Alba

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  1. José García-Nieto
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  2. Enrique Alba
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Correspondence to José García-Nieto.

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Communicated by V. Loia.

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García-Nieto, J., Alba, E. Hybrid PSO6 for hard continuous optimization. Soft Comput 19, 1843–1861 (2015). https://doi.org/10.1007/s00500-014-1368-8

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