Sine cosine algorithm (SCA) is a newly developed optimization tool that has received increasing attention in the field of computational intelligence. Nevertheless, SCA has a combination of strong exploration and weak exploitation abilities, which undermines the performance of the algorithm. A dynamic selection strategy (DSS) has been proposed in this paper to overcome this limitation. The central idea is that the selection pressure of the parent is dynamically regulated during the course of evolution, and the parent with higher fitness have higher selection probability. Accordingly, the weak exploitation ability of SCA can be enhanced. This approach can be readily applied to other SCA variants as well. Results from systematic experiments conducted on CEC2014 competition benchmark suite show that the proposed DSS method can significantly improve the performance of the SCAs. In addition, the proposed DSS-SCA algorithm has been successfully applied to six engineering design problems, which shows that the algorithm has the potential to solve other practical problems.
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