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Spider monkey optimization algorithm for constrained optimization problems

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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.

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Acknowledgments

The first author would like to thank Ministry of Human Resource Development, Govt. of India, for funding this research under the Grant No. MHR-02-23-200-44.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Kavita Gupta & Kusum Deep

  2. Department of Applied Mathematics, South Asian University, Akbar Bhawan, Chankyapuri, New Delhi, 110021, India

    Jagdish Chand Bansal

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  1. Kavita Gupta
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  2. Kusum Deep
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  3. Jagdish Chand Bansal
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Correspondence to Kavita Gupta.

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Communicated by A. Di Nola.

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Gupta, K., Deep, K. & Bansal, J.C. Spider monkey optimization algorithm for constrained optimization problems. Soft Comput 21, 6933–6962 (2017). https://doi.org/10.1007/s00500-016-2419-0

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