基于混沌反向学习和水波算法改进的白鲸优化算法

Improved beluga whale optimization algorithm based onchaotic inverse learning and water wave algorithm

针对原始的白鲸算法(beluga whale optimization,BWO)在某些情况下,中后期的探索和开发能力不足、多样性和求解精度降低、容易陷入局部最优等问题,提出一种基于混沌反向学习和水波算法改进的白鲸优化算法(TWBWO),进一步提高白鲸算法的计算精度和收敛速度,增强全局搜索和跳出局部最优能力。结合混沌映射和反向学习策略提高种群的质量和多样性,加快收敛速度。引入水波算法(water wave optimization,WWO)的折射操作,避免寻优时轻易陷入局部最优,提高计算精度。实验结果表明,TWBWO算法较之原始算法和其他经典算法在收敛速度和求解精度以及稳定性方面更为优秀,性能和寻优能力更强。

To address the problems of the original BWO algorithm,such as insufficient exploration and exploitation ability in the middle and late stages in some cases,reduce the diversity and solution accuracy,easy to fall into local optimality,this paper proposed a white whale optimization algorithm(TWBWO)based on chaotic backward learning and water wave algorithm improvement.Further it improved the computational accuracy and convergence speed of Moby Dick algorithm,enhanced the ability of global search and jumping out of local optimum.Combining chaotic mapping and backward learning strategies,it improved the quality and diversity of populations and speeded up the convergence rate.It introduced the refraction operation of the WWO to avoid the algorithm from repeatedly falling into local optima and improve the computational accuracy.The experimental results show that the TWBWO algorithm is superior to the original algorithm and other classical algorithms in terms of convergence speed and solution accuracy as well as stability,with better performance and better finding ability.