A Multi-population Water Wave Optimization Algorithm
Baohang Zhang, Haichuan Yang, Junyan Yi, Zhiming Zhang, Shangce Gao · 2021
The water wave optimization algorithm (WWO) is a new swarm intelligence searching method inspired by shallow water wave theory. It has the advantages of small population size and simple parameter configuration. But it still has the risks of slow convergence rate and low solution precision. In this paper, an inter-population cooperation strategy based on two populations is proposed for WWO. The sub-population is used to record the best individuals in the search processes and to guide the iteration of the main population. The above operations also enhance the ability of the algorithm, which can better balance exploration and exploitation. The proposed SPWWO algorithm is compared with the classical WWO algorithm and three other optimization algorithms on CEC2017 benchmarks. The results show that the SPWWO has good optimization ability and a fast convergence rate.