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An improvised grey wolf optimiser for global optimisation problems

Author

Listed:
  • Sarada Mohapatra
  • Priteesha Sarangi
  • Prabhujit Mohapatra

Abstract

The grey wolf optimisation (GWO) algorithm is one of the popular meta-heuristic algorithms in evolutionary computation. However, the GWO algorithm has many drawbacks such as less accuracy, incapable of local searching competence, and low convergence speed. Therefore, in this paper an improvised grey wolf optimisation algorithm called IGWO is being introduced to compensate for these drawbacks of the GWO method by altering the surrounding behaviour along with the new position updating formula. Several well-known benchmark functions are considered to examine the accurateness and convergence of the modified version. The outcomes are analogised to the well-known algorithms like particle swarm optimisation algorithm, GWO algorithm, mean GWO algorithm, fast evolutionary programming and gravitational search algorithm. The experimental results showed that the newly modified IGWO can produce extremely superior results in terms of optimum objective functions and convergence speediness.

Suggested Citation

  • Sarada Mohapatra & Priteesha Sarangi & Prabhujit Mohapatra, 2023. "An improvised grey wolf optimiser for global optimisation problems," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 26(2), pages 263-281.
    • Handle: RePEc:ids:ijmore:v:26:y:2023:i:2:p:263-281
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