Water Flow Optimizer: A Nature-Inspired Evolutionary Algorithm for Global Optimization
提出一种模拟水流从高地流向低地现象的水流优化器，通过层流和湍流两种算子实现全局优化，并在航天器轨迹优化问题中成功应用。
Inspired by the shape of water flow in nature, a novel algorithm for global optimization, water flow optimizer (WFO), is proposed. The optimizer simulates the hydraulic phenomena of water particles flowing from highland to lowland through two operators: 1) laminar and 2) turbulent. The mathematical model of the proposed optimizer is first built, and then its implementation is described in detail. Its convergence is strictly proved based on the limit theory. The parametric effect is investigated. The performance of the proposed optimizer is compared with that of the related metaheuristics on an open test suite. The experimental results indicate that the proposed optimizer achieves competitive performance. The proposed optimizer was also successfully applied to solve the spacecraft trajectory optimization problem.