《计算机应用研究》|Application Research of Computers

动态搜索和协同进化的鲸鱼优化算法

Whale optimization algorithm based on dynamic search and cooperative learning

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作者 张水平,高栋
机构 江西理工大学 信息工程学院,江西 赣州 341000
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文章编号 1001-3695(2020)09-016-2645-06
DOI 10.19734/j.issn.1001-3695.2019.05.0119
摘要 针对基本鲸鱼优化算法寻优精度低、收敛速度慢及容易陷入局部最优等缺陷,提出了一种动态搜索和协同进化的鲸鱼优化算法。首先,通过等价替换和Faure序列提高初始解的质量;其次,通过对种群进行分工,提高种群多样性并增强算法跳出局部最优解的能力;最后,根据种群进化信息动态调整搜索策略,从而提高算法的收敛速度和寻优精度。仿真实验结果表明,提出的改进算法相比基本鲸鱼优化算法和部分改进算法具有较好的寻优性能。
关键词 鲸鱼优化算法; 群智能算法; 收敛精度; 动态搜索
基金项目 国家自然科学基金资助项目(61562037)
江西省教育厅科学技术研究项目(GJJ180442)
江西省研究生创新专项基金资助项目(YC2018-S330)
本文URL http://www.arocmag.com/article/01-2020-09-016.html
英文标题 Whale optimization algorithm based on dynamic search and cooperative learning
作者英文名 Zhang Shuiping, Gao Dong
机构英文名 School of Information Engineering,Jiangxi University of Science & Technology,Ganzhou Jiangxi 341000,China
英文摘要 Aiming at the shortages of the basic whale optimization algorithm with low optimization precision, slow convergence speed and easy to fall into local optimum, this paper proposed a new algorithm based on dynamic search and cooperative learning. Firstly, it used equivalent replacement and Faure sequences to enhance the quality of initial solution. Secondly, it improved the population diversity and enhanced the ability to jump out of local optimal solutions through the division of population. Finally, in order to improve the convergence rate and precision, it dynamically adjusted the search strategy of the algorithms according to the population evolutionary information. The experimental results show that the proposed algorithm is much better than basic whale optimization algorithm and its several improved algorithms in optimization performance.
英文关键词 whale optimization algorithm; swarm intelligent algorithm; convergence precision; dynamic search
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收稿日期 2019/5/9
修回日期 2019/6/27
页码 2645-2650,2655
中图分类号 TP301.6
文献标志码 A