一种求解函数优化问题的改进鲸鱼优化算法  

Improved whale optimization algorithm for solving function optimization problems

作　　者：刘亮 何庆[1,2] Liu Liang;He Qing(College of Big Data&Information Engineering,Guizhou University,Guiyang 550025,China;Guizhou Provincial Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025 [2]贵州大学贵州省公共大数据重点实验室,贵阳550025

出　　处：《计算机应用研究》2020年第4期1004-1009,共6页Application Research of Computers

基　　金：贵州省科技计划项目重大专项资助项目(黔科合重大专项字[2018]3002);贵州省公共大数据重点实验室开放课题(2017BDKFJJ004);贵州省教育厅青年科技人才成长项目(黔科合KY字[2016]124);贵州大学培育项目(黔科合平台人才[2017]5788)。

摘　　要：为提高鲸鱼优化算法求解复杂函数优化问题的性能,提出一种基于自适应参数及小生境技术的改进鲸鱼优化算法。首先,引入自适应概率阈值协调算法的全局探索及局部开发能力;其次,利用自适应位置权重对鲸鱼位置更新公式进行调整,提高算法的收敛速度及寻优精度;最后,采用预选择小生境技术,避免算法出现早熟收敛的现象。通过对12个典型基准测试函数的仿真表明,改进算法的寻优精度和收敛速度较对比算法均有明显提升,证明了提出的改进策略能有效提高鲸鱼优化算法求解复杂函数优化问题的性能。In order to improve the performance of whale optimization algorithm for solving complex function optimization problems,this paper proposed an improved whale optimization algorithm based on adaptive parameters and niche technology.Firstly,the algorithm introduced an adaptive probability threshold to coordinate the global exploration and local development abilities.Then,the algorithm used adaptive position weights to adjust the whale position update formula to improve the convergence speed and search precision.Finally,the algorithm used preselection niche technology to avoid premature convergence.The results on 12 typical benchmark functions show that the improved algorithm has faster convergence speed and higher search precision than other comparison algorithms.It proves that the improvement strategy can effectively improve the performance of the whale optimization algorithm for solving complex function optimization problems.

关 键 词：函数优化 鲸鱼优化算法 自适应参数 小生境 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]