A New Enhanced Hybrid Grey Wolf Optimizer (GWO) Combined with Elephant Herding Optimization (EHO) Algorithm for Engineering Optimization

Document Type : Regular Article

Authors

1 Department of Engineering, Ale Taha Institute of Higher Education, 14888-36164, Tehran, Iran

2 School of Civil and Environmental Engineering, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, South Korea

10.22115/scce.2022.342360.1436

Abstract

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms.

Highlights

  • A new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm is proposed.
  • A novel separating operator is introduced to help the population for jumping out of the local optima.
  • Some mathematical and engineering benchmark problems are used to validate the performance of the proposed GWOEHO.
  • Wilcoxon's rank-sum test results revealed that GWOEHO outperforms other algorithms in most function minimization.

Keywords

Main Subjects


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  • Receive Date: 14 May 2022
  • Revise Date: 18 July 2022
  • Accept Date: 31 August 2022
  • First Publish Date: 31 August 2022