融合改进DBSCAN聚类和多种进化策略的改进蝗虫优化算法
Improved Grasshopper Optimization Algorithm Combining Improved DBSCAN Clustering and Multiple Evolutionary Strategies
于平1
作者信息
摘要
针对蝗虫优化算法复杂高维问题收敛精度不高、寻优能力不强、难以跳出局部最优的缺陷,提出一种融合改进DBSCAN聚类和多种进化策略的改进蝗虫优化算法(GOA).首先,引入多核加权距离度量和动态并行运算策略,以提高改进DBSCAN高维数据聚类效率.其次,利用改进DBSCAN可以对任意形状数据集进行聚类的优势,对蝗虫种群进行聚类分析,并为蝗虫个体赋予核心点、边界点和孤立点等空间属性.最后,综合考虑种群空间特性和个体间进化程度差异性,设计多种蝗虫个体进化策略,以更好地提升算法全局寻优能力.典型复杂、高维测试函数以及经典TSP问题仿真结果表明:改进后的GOA在收敛精度上更具优势.
Abstract
An improved grasshopper optimization algorithm(GOA)combining improved DBSCAN clustering and multiple evolutionary strategies was proposed to overcome the shortcomings in complex high-dimensional problems,such as low convergence accuracy,weak optimization ability,and difficulty in jumping out of local optima.Firstly,by introducing multi-core weighted distance measurement and dynamic parallel operation strategy,the clustering efficiency of DBSCAN(density based spatial cluste-ring of application with noise)for high-dimensional data was improved.Secondly,utilizing the advantage for arbitrarily shaped datasets,the DBSCAN was used to analyze the GOA population,and the grasshoppers were endowed with spatial attributes such as core points,boundary points,and isolated points.Finally,taking into account the spatial characteristics of the population and the differences in the degree of evolution between individuals,various individual evolution strategies for locusts were designed to improve the global optimization ability of GOA.The simulation results of typical complex,high-dimensional test functions and classic TSP problems show that the improved GOA has more advantages in convergence accuracy.
关键词
蝗虫优化算法/DBSCAN/聚类/收敛精度Key words
grasshopper optimization algorithm/DBSCAN/clustering/convergence accuracy引用本文复制引用
出版年
2024
NETL
NSTL
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