Whale optimization algorithm (WOA) has the advantages of simple implementation, few adjustment parameters, strong optimization ability, and fast convergence speed, and has been widely used. Currently, it is mainly used in image segmentation, feature selection, model prediction, path planning, production scheduling and other fields. But there are also shortcomings such as easy to fall into local optimum and low convergence accuracy. Therefore, research on the improved whale algorithm has important theoretical significance and practical value. This article describes the basic principles, algorithm flow and characteristics of the whale optimization algorithm, analyzes the range of algorithm parameters and their impact on algorithm performance, and reviews the algorithms in recent years from three aspects: algorithm hybridization, operator improvement, and parameter optimization. The improvement ideas and development trends of the algorithm point out the next research direction of the algorithm.
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