基于自适应参数调节和动态分组学习的水波优化算法

Water Wave Optimization Algorithm with Adaptive Parameter Adjustment and Dynamic Group Learning

针对浅水波理论易陷入局部最优、收敛速度慢的问题,提出基于自适应参数调节和动态分组学习的水波优化算法。通过分析控制参数的变化,采取了参数自适应调节机制平衡算法的全局搜索和局部开发能力;设计基于正余弦因子的动态分组学习阶段,有效增强了算法跳出局部最优的能力,从而提高了算法的收敛精度。仿真结果表明,与标准水波优化算法相比,改进的算法表现出了较好的竞争性。

Aiming at the problems that shallow water wave theory is easy to fall into local optimum and slow convergence speed,water wave optimization algorithm with adaptive parameter adjustment and dynamic group learning is proposed.By analyzing the changes of control parameters,parameter adaptive adjustment mechanism is adopted to balance the global search and local development capabilities of the algorithm;a dynamic grouping learning stage based on the sine and cosine factors is designed,which effectively enhances the algorithm’s ability to jump out of the local optimum,and finally improves the convergence accuracy of the algorithm.Simulation experiment results show that compared with the standard water wave optimization algorithm,the improved algorithm shows better competition.