An Adaptive Water Wave Optimization Algorithm with Enhanced Wave Interaction

摘要

Water Wave Optimization (WWO), as a nature-inspired optimization algorithm, has received much attention. In this paper, we tend to improve the algorithm by proposing an adaptive WWO with enhanced wave interaction (AWWOEI). In the proposed method, two operators (namely, Gaussian-based propagation and refraction learning) with an adaptive mechanism are introduced to enhance the wave interaction in the algorithm. The first operator, Gaussian-based propagation operation, is designed to strengthen the exploration ability of the algorithm by encouraging each individual to learn from different exemplars. While, the second operator, refraction learning, aims to improve the exploitation capability of WWO. Further, rather than using a fixed breaking coefficient, an adaptive mechanism has been employed to dynamically adjust its values during evolution. Experiments have been carried out to evaluate the performance of the proposed method and compare it with related methods. The results have demonstrated the superiority of the proposed method.