一种增强型鲸鱼优化算法

An Enhanced Whale Optimization Algorithm

鲸鱼优化算法(whale optimization algorithm,WOA)具有独特的搜索机制,非常适合解决复杂的优化问题。针对基本鲸鱼优化算法收敛速度慢、容易陷入局部最优的缺点,提出了一种增强型鲸鱼优化算法(enhanced whale optimization algorithm,EWOA)。首先通过引入非线性时变的自适应权重,提升了鲸鱼算法在全局探索和局部寻优阶段的性能;其次在鲸鱼算法的收缩包围阶段引入差分变异微扰因子,避免了算法出现早熟收敛现象;另外改进了鲸鱼个体的对数螺旋搜索方式,提高了算法遍历求解的能力。实验结果表明,所提算法的寻优精度和收敛速度较基本鲸鱼算法有较大提升。

The whale optimization algorithm is suitable for solving complex optimization problem because it has unique search mechanism.The standard whale optimization algorithm has many shortcomings such as slow convergence speed and easy to fall into a local optimum.In this paper,an enhanced whale optimization algorithm(EWOA)is proposed.Firstly,a non-linear time-variant adaptive weight was introduced to improve the global search capability and local search capability;secondly,a differential mutation perturbation factor was introduced in the stage of encircling prey to avoid premature convergence;additionally,the searching path of Logarithmic Spiral curve was improved to enhance the ergodicity of the algorithm.The results show that the EWOA significantly outperforms the standard WOA in searching precision and convergence speed.