基于Iterative映射和非线性拟合的鲸鱼优化算法

为解决鲸鱼优化算法中收敛速度慢和寻优精度低等问题，提出一种基于Iterative映射和非线性拟合的鲸鱼优化算法（NWOA）。首先，该算法利用了Iterative映射对鲸鱼种群初始化，保证初始种群的多样性；其次，采用非线性拟合的策略对收敛因子和惯性权重进行改进，以平衡算法的全局勘测能力和局部开发能力。通过对13种函数进行仿真实验，从均方差和平均值的角度分析，改进后算法寻优精度显著提高，且稳定性较强。实验结果表明NWOA与传统的鲸鱼优化算法相比，收敛速度明显加快。

Whale optimization algorithm based on iterative mapping and nonlinear fitting

In order to solve the problems of slow convergence speed and low optimization accuracy in whale optimization algorithm, a whale optimization algorithm based on iterative mapping and nonlinear fitting(NWOA) is proposed. Firstly, iterative mapping is taken advantage to initialize whale population, which guarantees initial population diversity. Secondly, nonlinear fitting strategy is used to improve the convergence factor and inertia weight to balance the global survey ability and local development ability of the algorithm. Through the simulation test of 13 functions, the improved algorithm has a significant improvement in precision and stability from the point of mean square error and average value. The experimental results show that the convergence speed of the algorithm is faster than that of the traditional whale optimization algorithm.