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Spider Monkey Optimization algorithm for numerical optimization

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Abstract

Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission–fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.

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Acknowledgments

The authors acknowledge the anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Jagdish Chand Bansal & Shimpi Singh Jadon

  2. South Asian, University, New Delhi, India

    Jagdish Chand Bansal & Harish Sharma

  3. Independent Consultant in Optimization, Groisy, France

    Maurice Clerc

Authors
  1. Jagdish Chand Bansal
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  2. Harish Sharma
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  3. Shimpi Singh Jadon
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  4. Maurice Clerc
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Correspondence to Jagdish Chand Bansal.

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Bansal, J.C., Sharma, H., Jadon, S.S. et al. Spider Monkey Optimization algorithm for numerical optimization. Memetic Comp. 6, 31–47 (2014). https://doi.org/10.1007/s12293-013-0128-0

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  • DOI: https://doi.org/10.1007/s12293-013-0128-0

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