基于双重动态调整的改进非洲秃鹫优化算法
Improved African vulture optimization algorithm based on dual dynamic adjustment
陈麒羽 1邵洁 1王超群 2陈乐 2邰兴雨3
作者信息
- 1. 上海电力大学电子与信息工程学院 上海 200090
- 2. 航天智慧能源研究院 上海 201201
- 3. 上海航天能源股份有限公司 上海 201201
- 折叠
摘要
针对非洲秃鹫优化算法(African vulture optimization algorithm,AVOA)多样性低、探索开发能力不平衡、易发生早熟的现象,提出了一种基于双重动态调整的改进非洲秃鹫优化算法(improvement African vulture optimization algorithm,IA-VOA).改进后的算法分为3个部分,通过引入混沌映射初始化种群,以确保种群在前期寻优中具有较高的多样性;加入动态调整因子来确定当前最优个体,用来平衡前期探索与后期开发的能力;针对AVOA中饥饿率的变化情况加入动态调整的高斯扰动,用于防止早熟问题的发生,提高最终解的质量.改进后的算法在9个标准测试函数上进行测试.结果表明,该算法表现出更佳的求解性能.
Abstract
Aiming at the phenomena of low diversity,unbalanced exploration and exploitation ability,and susceptibility to premature maturity in African vulture optimization algorithm(AVOA),an improved African vulture optimization algorithm(IAVOA)based on double dynamic adjustment is proposed.The improved algorithm is divided into three parts,initializing the population by introducing chaotic mapping to ensure that the population has high diversity in the pre-optimization;adding dynamic adjustment factors to determine the current optimal individual,which is used to balance the ability of pre-exploration and post-exploitation;and adding dynamically adjusted Gaussian interference for the change of the starvation rate in the AVOA,which is used to prevent premature maturation and improve the quality of the final solution.In order to verify the effectiveness of the algorithm,the algorithm is subjected to simulation experiments on nine standard test functions.The results show that the algorithm exhibits better solution accuracy as well as convergence speed.
关键词
非洲秃鹫优化算法/高斯干扰/动态调整因子/混沌映射Key words
African vulture optimization algorithm/Gaussian interference/dynamic adjustment factor/chaos mapping引用本文复制引用
出版年
2024
NETL
NSTL
万方数据