A Chaotic Crow Search Algorithm for High-Dimensional Optimization Problems

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  Dunia S. Tahir   |      Ramzy S. Ali   |   

Crow Search Algorithm is an innovative meta-heuristic optimization algorithm. In this paper, chaotic maps are combined into Crow Search Algorithm to increase its global optimization. Ten variant chaotic maps are used and the Tent map is found as the best choices for high dimensional problems. The novel Chaotic Crow Search Algorithm is relied on the substitution of a random location of search space and the awareness parameter of crow with chaotic sequences. The results show that the chaotic maps are able to enhance the performance of the Crow Search Algorithm. Also the novel Chaotic Crow Search Algorithm outperforms the conventional Crow Search Algorithm, first version of Chaotic Crow Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization Algorithm from the point view of speed convergence and the function dimensions.