Hierarchical Water Wave Optimization

Water wave optimization algorithm (WWO) draws inspiration from the natural summary of the shallow water wave theory. It benefits from a modest population size and straightforward parameter design. However, WWO still has some performance problems that need to be solved, e.g., the convergence speed is too slow, and it cannot find the optimal point efficiently and accurately. This paper proposes a strategy of multi-level population structure for it, namely DWWO. The multi-level population structure strategy further enhances the balance between exploitation performance and exploration performance of the WWO algorithm. It makes the algorithm performance more stable, which leads to the DWWO algorithm can be used in more practical problems. DWWO algorithm is compared with the classical WWO algorithm, cuckoo search algorithm, sparrow search algorithm, and sine cosine algorithm on the basis of IEEE CEC2017 problem set. Comprehensive experimental results show that DWWO algorithm has better optimization ability and relatively fast convergence speed in comparison with other algorithms.