混合策略改进的鲸鱼优化算法

针对标准鲸鱼优化算法易出现搜索速度慢、寻优精度低及早熟收敛等问题，提出一种混合策略改进的鲸鱼优化算法。首先采用混沌映射生成初始种群增加种群多样性，为算法全局搜索奠定基础；然后引入非线性策略改进收敛因子和惯性权重，平衡算法的全局探索与局部开发能力并加快收敛速度；最后根据群体适应度方差设定阈值进行变异操作，避免算法出现早熟收敛的现象。通过对12个典型基准函数进行三方面的性能测试，实验结果表明，改进算法在搜索速度、收敛精度等方面有显著提高，且摆脱陷入局部最优解的能力强。

Improved whale optimization algorithm based on hybrid strategy

In order to solve the problems of slow search speed, premature convergence and low search accuracy of standard whale optimization algorithm, this paper proposed a hybrid strategy to improve whale optimization algorithm. Firstly, it increased the population diversity by generating the initial population with chaotic map, which laid a foundation for the algorithm global search. Then, by the non-linear strategy, it improved the convergence factor and inertia weight to balance the global exploration, local development ability of the algorithm and accelerated the convergence speed. Finally, according to the variance of the group fitness, it set the threshold performing the mutation operation to avoid the premature convergence of the algorithm. By testing 12 typical benchmark functions in three aspects, the experimental results show that the improved algorithm has a remarkable enhancement in search speed and convergence accuracy. Besides, it has a strong ability to get rid of falling into local optimum.