摘要
针对鲸鱼算法存在初始种群多样性差、迭代中后期种群呈趋同化易陷入局部最优解的问题,提出一种基于T-分布扰动的自适应鲸鱼算法(CWOA),通过Tent混沌映射优化初始种群使搜索范围具有遍历性,引入正弦变化因子平衡算法全局搜索与局部开发能力以提高算法的搜索精度,通过T-分布函数对趋同种群进行扰动提高算法跳出局部最优解的能力。实验表明,CWOA算法与传统鲸鱼算法与其他经典算法相比具有更高的寻优精度,能更快地跳出局部最优解。
Aiming at the problems of poor initial population diversity in whale algorithm and population convergence in late iteration,an adaptive whale algorithm(CWOA)based on T-distribution perturbation is proposed in this paper to optimize the initial population through Tent chaos mapping to make the search range ergodic.The global search and local development capabilities of the sinusoidal variable factor balance algorithm are introduced to improve the search accuracy of the algorithm,and the convergent population is disturbed by T-distribution function to improve the ability of the algorithm to jump out of the local optimal solution.Experiments show that CWOA algorithm has higher optimization accuracy and can jump out of the local optimal solution faster than the traditional whale algorithm and other classical algorithms.
出处
《工业控制计算机》
2024年第10期110-112,共3页
Industrial Control Computer
基金
陕西工业职业技术学院科研基金资助项目(2023YKYB-006)。
