嵌入Circle映射和逐维小孔成像反向学习的鲸鱼优化算法

针对鲸鱼优化算法(WOA)容易陷入局部最优解、收敛速度慢等缺陷,提出一种改进鲸鱼优化算法.首先,利用Circle混沌序列取代原始算法中随机产生的初始种群,提高初始个体的多样性;其次,提出一种逐维小孔成像反向学习策略,增加寻优位置的多样性,提高算法摆脱局部最优的能力;最后,提出融合贝塔分布和逆不完全$\varGamma$函数的自适应权重,在保留鲸鱼优化算法优点的前提下,协调算法的搜索能力.通过对10个基准函数进行仿真实验,同时使用Wilcoxon检验、MAE等方法来评价改进后鲸鱼优化算法的性能,实验结果表明,改进算法在求解效率和稳定性等方面都有较大提升,同时,算法的寻优精度和收敛速度也比原始算法更优秀.

Whale optimization algorithm for embedded Circle mapping and one-dimensional oppositional learning based small hole imaging

Aiming at the disadvantages of the whale optimization algorithm(WOA), such as easy to fall into local optimal solution and slow convergence speed, an improved whale optimization algorithm is proposed. Firstly, Circle chaotic sequence is used to replace the initial population randomly generated in the original algorithm to improve the diversity of the initial individuals. Then, a one-dimensional small hole imaging reverse learning strategy is proposed to increase the diversity of the optimal location and improve the ability of the algorithm to get rid of the local optimum. Finally, the adaptive weights of fusion Beta distribution and inverse incomplete $\varGamma$ function are proposed which can coordinate the search ability of the algorithm under the premise of retaining the advantages of the whale optimization algorithm. The performance of the improved whale optimization algorithm is evaluated using Wilcoxon test, MAE and other methods through simulation experiments on 10 benchmark functions. The experimental results show that the improved algorithm has a great improvement in solving efficiency and stability, and the optimization accuracy and convergence speed of the algorithm are also better than the original algorithm.