An Improved Crow Search Algorithm with Grey Wolf Optimizer for High-Dimensional Optimization Problems


Abudayor A., NALBANTOĞLU Ö. U.

3rd International Conference on Soft Computing and its Engineering Applications, icSoftComp 2021, Anand, India, 10 - 11 December 2021, vol.1572 CCIS, pp.51-64 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1572 CCIS
  • Doi Number: 10.1007/978-3-031-05767-0_5
  • City: Anand
  • Country: India
  • Page Numbers: pp.51-64
  • Keywords: Crow search algorithm, Grey-wolf optimizer, High-dimensional optimization problem, Hybrid approach, Optimization
  • Kayseri University Affiliated: No

Abstract

© 2022, Springer Nature Switzerland AG.Crow search algorithm (CSA) mainly solves optimization problems. In high-dimensional optimization problems, CSA searches with moves toward the wrong crows’ hiding position. Solving the problems of the CSA algorithm, this paper proposes an improved CSA with Grey Wolf Optimization (GWO) algorithms is called ICSAGWO for manipulating the high-dimensional optimization problem. The main idea is to hybrid both algorithms’ strengths that utilize the efficient exploitation ability of CSA with good performance in the exploration ability and convergence speed of GWO. By hybridizing, the authors employ an adaptive inertia weight to control exploitation and exploration capacities. ICSAGWO algorithm is tested on twenty-three benchmark functions with 30 to 500 dimensions and compared among other algorithms, such as GSA, WOA, GWO, CSA, etc. Experimental results of the proposed algorithm ICSAGWO obtain high performance in both unimodal and multimodal and not affecting the search performance even in high dimension data over other algorithms.