Abstract
The water wave optimization (WWO) algorithm is a new cluster intelligence search method. It has the advantages of a small population size and simple parameter configuration. It is used to build an efficient mechanism for searching in high-dimensional solution spaces. However, it has a proclivity for becoming stuck in local optima. Coincidentally, the sparrow search algorithm (SSA) has good exploration ability. By combining WWO and SSA, we propose a hybrid algorithm, called WWOSSA. The experimental results of the WWOSSA algorithm based on 29 benchmark functions of IEEE CEC2017 have good optimization ability and a fast convergence rate.
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