Nature-inspired approach: a wind-driven water wave optimization algorithm - Zhang, Jinzhong; Zhou, Yongquan; Luo, Qifang (Applied intelligence (Dordrecht, Netherlands), 2019)
The water wave optimization (WWO) based on shallow water wave theory is a new nature-inspired optimization algorithm that imitates wave motion to solve optimization problems. But basic WWO has some disadvantages, such as low calculation accuracy and falling easily into the local optimum. The WDO based on the motion of air parcels in the wind mainly imitates the process of air pressure balance such that different air pressure in different regions leads to air flow. The WDO demonstrates strong global search ability in the entire search space. To improve the optimization performance of WWO and avoid premature convergence, the velocity of WDO is introduced into the propagation operation, which quickens water wave motions to determine the global optimal solution. In this paper, the wind-driven WWO (WDWWO) algorithm is proposed, which not only achieves complementary advantages, but also balances exploration and exploitation. Sixteen benchmark test functions were used to detect calculation accuracy of WDWWO. To verify the robustness and effectiveness of WDWWO, it was applied to solve three engineering design problems. The experimental results reveal that the proposed algorithm is able to solve challenging engineering optimization problems and is a very competitive algorithm as compared with other optimization algorithms.