融合差分进化与多策略的阿基米德优化算法
Archimedes Optimization Algorithm Combining Differential Evolution and Multi Strategy
徐小平 1张钰 1王峰 2苏李君1
作者信息
- 1. 西安理工大学 理学院,陕西 西安 710054
- 2. 西安交通大学数学与统计学院,陕西 西安 710049
- 折叠
摘要
鉴于阿基米德优化算法存在易早熟,收敛慢等缺点,提出一种融合差分进化与多策略的阿基米德优化算法.首先,通过位置参数,随机选择两种混沌映射初始化种群来增强种群的多样性;其次,通过余弦控制因子的动态边界策略改进密度因子,来平衡算法的全局探索与局部开发能力;接着,融合差分进化算法,缩小最优位置的范围,以达到快速向最优位置靠拢的目的.最后,选取10个基准测试函数进行仿真实验,并对实验结果进行Wilcoxon秩和检验,结果表明所提算法性能优于对比算法.
Abstract
In view of the shortcomings of the Archimedes optimization algorithm,such as prematurity and slow convergence,this paper proposes an Archimedes optimization algorithm which combines differential evolution and multi-strategy.Firstly,two chaotic maps were ran-domly selected to initialize the population through location parameters to enhance the diver-sity of the population.Secondly,the dynamic boundary strategy of cosine control factor is used to improve the density factor to balance the global exploration and local development ability of the algorithm.Then,the differential evolution algorithm is integrated to narrow the range of the optimal position,so as to achieve the purpose of getting closer to the optimal po-sition quickly.Finally,10 benchmark test functions were selected for simulation experiments,and the experimental results were tested by Wilcoxon rank sum test.The results show that the proposed algorithm performs better than the comparison algorithm.
关键词
阿基米德优化算法/差分进化算法/混沌映射/余弦控制因子/动态边界Key words
Archimedes optimization algorithm/differential evolution algorithm/chaotic mapping/cosine control factor/dynamic boundary引用本文复制引用
基金项目
陕西省创新能力支撑计划(2020PT-023)
陕西省重点产业创新链(群)-工业领域项目(2020ZDLGY04-04)
出版年
2024
NETL
NSTL
万方数据