Binary Chaotic Crow Search Algorithm

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel metaheuristic optimizer namely chaotic crow search algorithm (CCSA) is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for twenty benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizing the classification performance and minimizing the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.