Abstract: Crow Search Algorithm (CSA) is a swarm-based single-objective optimizer proposed in recent years, which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed. The original version of the CSA has simple parameters and moderate performance. However, it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases. Therefore, strategies of mutation and crisscross are combined into CSA (CCMSCSA) in this paper to improve the performance and provide an efficient optimizer for various optimization problems. To verify the superiority of CCMSCSA, a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions. The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum. In addition, the scalability of CCMSCSA is analyzed, and the algorithm is applied to several engineering problems in a constrained space and feature selection problems. Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems. The proposed CCMSCSA performs well in almost all experimental results. Therefore, we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.

Key words: Crow search algorithm , · Feature selection , · Global optimization , · Metaheuristic algorithms , · Engineering problems , · Bionic algorithm