Abstract：In order to solve the problems of slow convergence speed,low convergence accuracy and easy to fall into local optima solution,a whale optimization algorithm based on elite opposition-based and crisscross optimization is proposed.Firstly,the elite opposition-based learning strategy is used to initialize the population in order to improve the quality of the initial solution and speed up the global convergence speed.Secondly,the inverse incomplete Γ function is used to update convergence factor to balance the global exploration and local development ability of the algorithm.Finally,the crisscross strategy is used to modify the population and the global optimal solution,so as to maintain the diversity of the individual population and improve the ability of the algorithm to jump out of the local optimum.The simulation results of eight classical test functions show that the ECWOA algorithm has obvious improvement in convergence accuracy and convergence speed.
刘琨,赵露露,王辉. 一种基于精英反向和纵横交叉的鲸鱼优化算法[J]. 小型微型计算机系统, 2020, 41(10): 2092-2097.
LIU Kun,ZHAO Lu-lu,WANG Hui. Whale Optimization Algorithm Based on Elite Opposition-based and Crisscross Optimization. Journal of Chinese Computer Systems, 2020, 41(10): 2092-2097.