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Hybrid Grey Wolf Optimizer with Mutation Operator

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 817))

Abstract

Grey Wolf Optimizer (GWO), developed by Mirjalili et al. (Adv Eng Softw 69:46–61, 2014 [1]), is a recently developed nature-inspired technique based on leadership hierarchy of grey wolves. In this paper, Grey Wolf Optimizer has been hybridized with differential evolution (DE) mutation, and two versions, namely DE-GWO and gDE-GWO, have been proposed to avoid the stagnation of the solution. To evaluate the performance of both the proposed versions, a set of 23 well-known benchmark problems has been taken. The comparison of obtained results between original GWO and proposed hybridized versions of GWO is done with the help of Wilcoxon signed-rank test. The results conclude that the proposed hybridized version gDE-GWO of GWO has better potential to solve these benchmark test problems compared to GWO and DE-GWO.

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Authors and Affiliations

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

    Shubham Gupta & Kusum Deep

Authors
  1. Shubham Gupta
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  2. Kusum Deep
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Corresponding author

Correspondence to Shubham Gupta .

Editor information

Editors and Affiliations

  1. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  2. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  3. Department of Mathematics and Computer Science, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya Nagar

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India

    Akshay Kumar Ojha

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Gupta, S., Deep, K. (2019). Hybrid Grey Wolf Optimizer with Mutation Operator. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_75

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