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Soft Computing

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02.11.2016 | Foundations

Spider monkey optimization algorithm for constrained optimization problems

verfasst von: Kavita Gupta, Kusum Deep, Jagdish Chand Bansal

Erschienen in: Soft Computing | Ausgabe 23/2017

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Abstract

In this paper, a modified version of spider monkey optimization (SMO) algorithm for solving constrained optimization problems has been proposed. To the best of author’s knowledge, this is the first attempt to develop a version of SMO which can solve constrained continuous optimization problems by using the Deb’s technique for handling constraints. The proposed algorithm is named constrained spider monkey optimization (CSMO) algorithm. The performance of CSMO is investigated on the well-defined constrained optimization problems of CEC2006 and CEC2010 benchmark sets. The results of the proposed algorithm are compared with constrained versions of particle swarm optimization, artificial bee colony and differential evolution. Outcome of the experiment and the discussion of results demonstrate that CSMO handles the global optimization task very well for constrained optimization problems and shows better performance in comparison with compared algorithms. Such an outcome will be an encouragement for the research community to further explore the potential of SMO in solving benchmarks as well as real-world problems, which are often constrained in nature.

Vorheriger Artikel Comparing dependent combination rules under the belief classifier fusion framework

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Titel: Spider monkey optimization algorithm for constrained optimization problems
verfasst von: Kavita Gupta
Kusum Deep
Jagdish Chand Bansal
Publikationsdatum: 02.11.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 23/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2419-0

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