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Comparative Analysis of Selected Variant of Spider Monkey Optimization Algorithm

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Advances in Computing and Intelligent Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Spider Monkey Optimization (SMO) algorithm is recently developed optimiser that is stimulated by the extraordinary social activities of spider monkeys known as fission–fusion social structure. The SMO is developed to find the solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. Here, three modifications in SMO algorithm are selected for the purpose of comparison, namely, exponential SMO, chaotic SMO and sigmoidal SMO. These modifications suggested new strategies for selecting perturbation rate in local leader and local leader decision phase. These strategies replaced linear approach of perturbation by nonlinear functions. The proposed strategies are tested over a set of benchmark functions.

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Author information

Authors and Affiliations

  1. Jagannath University, Jaipur, Rajasthan, India

    Basudev Sharma & Vivek Kumar Sharma

  2. Amity University, Jaipur, Rajasthan, India

    Sandeep Kumar

Authors
  1. Basudev Sharma
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  2. Vivek Kumar Sharma
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  3. Sandeep Kumar
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Corresponding author

Correspondence to Sandeep Kumar .

Editor information

Editors and Affiliations

  1. Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. University of Southern Denmark, Odense, Denmark

    Kannan Govindan

  3. Amity University, Jaipur, Rajasthan, India

    Ramesh C. Poonia

  4. Amity University, Jaipur, Rajasthan, India

    Sandeep Kumar

  5. University of Bahrain, Zallaq, Bahrain

    Wael M. El-Medany

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Sharma, B., Sharma, V.K., Kumar, S. (2020). Comparative Analysis of Selected Variant of Spider Monkey Optimization Algorithm. In: Sharma, H., Govindan, K., Poonia, R., Kumar, S., El-Medany, W. (eds) Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0222-4_33

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