摘要
提出一种基于精英反向学习的正余弦指数分布优化算法(IEDO).IEDO算法引入精英反向学习策略、柯西-高斯变异策略和正余弦策略,提高了算法的收敛精度.将IEDO应用于齿轮设计问题中并进行对比,结果显示,IEDO在工程问题中具有较好的应用性.
A Sincosine Exponential Distribution Optimization Algorithm(IEDO)based on elite inverse learning is proposed.The IEDO algorithm introduces the elite reverse learning strategy,the Cauchy-Gaussian mutation strategy and the sine and cosine strategy,which improves the convergence accuracy of the algorithm.The IEDO was applied to the gear design problem and compared.The results showed that IEDO had better applicability in engineering problems.
作者
王一荻
陈丽敏
叶汶建
沈越
WANG Yidi;CHEN Limin;YE Wenjian;SHEN Yue(Mudanjiang Normal University School of Mathematical Science,Mudanjiang 157011,China;Mudanjiang Normal UniversityInstitute of Applied Mathematics,Mudanjiang 157011,China;Mudanjiang Normal UniversitySchool of Computer and Information Technology,Mudanjiang 157011,China)
出处
《牡丹江师范学院学报(自然科学版)》
2025年第3期6-10,共5页
Journal of Mudanjiang Normal University:Natural Sciences Edition
基金
黑龙江省教育厅基本科研业务费项目(1451TD019)
黑龙江省自然科学基金联合基金重点项目(ZL2024A001)。
关键词
指数分布优化算法
精英反向学习策略
柯西-高斯变异策略
正余弦策略
exponential distribution optimization algorithm(EDO)
elite inverse learning strategy
Cauchy-Gauss-ian mutation strategy
sine and cosine strategy
