一种新的全局优化鲸鱼群算法(Global_WOA)

A new global optimization whale swarm algorithm(Global_WOA)

通过对鲸鱼群算法中勘探阶段和开发阶段的两个数学模型中的相关参数优化,提出一种新鲸鱼群智能优化算法的策略,该策略主要解决了算法在精度较低和收敛速度缓慢的问题,并在实际中应用。如目前研究比较热门的特征选择中,对应用结果有较高的提升。最后通过对6个典型基准测试函数的仿真来验证算法的有效性。

By optimizing the relevant parameters of the two mathematical models in the exploration stage and the development stage of the whale swarm algorithm,a new strategy of whale swarm intelligent optimization algorithm is proposed,which mainly solves the problems of low accuracy and slow convergence speed of the algorithm and is applied in practice.If it is applied in the selection of features that are currently hotly studied,the application results will be highly improved.Finally,the effectiveness of the algorithm is verified by simulating six typical benchmark functions.