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

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.