An efficient double adaptive random spare reinforced whale optimization algorithm

Abstract Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. In this paper, a reinforced variant called RDWOA is proposed to alleviate the central shortcomings of the original method that converges slowly, and it is easy to fall into local optimum when dealing with multi-dimensional problems. Two strategies are introduced into the original WOA. One is the strategy of random spare or random replacement to enhance the convergence speed of this algorithm. The other method is the strategy of double adaptive weight, which is introduced to improve the exploratory searching trends during the early stages and exploitative behaviors in the later stages. The combination of the two strategies significantly improves the convergence speed and the overall search ability of the algorithm. The advantages of the proposed RDWOA are deeply analyzed and studied by using typical benchmark examples such as unimodal, multi-modal, and fixed multi-modal functions, and three famous engineering design problems. The experimental results show that the exploratory and exploitative tendencies of WOA and its convergence mode have been significantly improved. The RDWOA developed in this paper is a promising improved WOA variant, and it has better efficacy compared to other state-of-the-art algorithms.