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Improved Grey Wolf Optimizer Based on Opposition-Based Learning

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Book cover Soft Computing for Problem Solving

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

Abstract

Swarm intelligence (SI)-based algorithms are very popular optimization techniques to deal with complex and nonlinear optimization problems. Grey wolf optimizer (GWO) is one of the newest and efficient algorithms based on hunting activity and leadership hierarchy of grey wolves. To avoid the slow convergence and stagnation problem in local optima, in this paper, opposition-based learning (OBL) is incorporated in GWO for the population initialization as well as for the iteration jumping. In this strategy, opposite numbers have been used to deal with the problem of slow convergence. The proposed algorithm is named as opposition-based explored grey wolf optimizer (OBE-GWO). To evaluate the performance of OBE-GWO, it is tested on some well-known benchmark problems. The experimental analysis concludes the better performance of OBE-GWO.

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Author information

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

Appendix

Appendix

Test Problems

figure d
figure e
figure f

Box Plot

See Fig. 1.

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Gupta, S., Deep, K. (2019). Improved Grey Wolf Optimizer Based on Opposition-Based Learning. 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_26

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