摘要
针对求解数值积分的计算实验,提出了一种混沌映射和单纯形扰动的改进灰狼优化算法。该方法的基本思想是在积分区域随机选取一定数量的节点,利用改进灰狼优化算法对这些节点进行优化,并将函数变化快的区间分割较细,函数变化慢的区间分割较粗,最后结合Simpson 3/8积分公式算得较为准确的数值积分。数值实验表明,该算法得到的积分值不但精确度高,而且收敛速度快,在工程计算领域中具有一定的应用价值。
An improved grey wolf optimization algorithm with chaos optimization and simplex method is proposed for solving numerical integration experiments.The basic idea of this method is to randomly select a certain number of points in the integration interval,use the improved grey wolf optimization algorithm to optimize these points,and segment the interval with rapid-changing function into smaller segments,while the interval with the slow-changing function into larger segments.Finally,the Simpson 3/8 formula is combined to calculate a more accurate numerical integral.Numerical experiments show that the integral value obtained by this algorithm is not only accurate,but also converges quickly.It has certain application value in the field of engineering calculation.
作者
黄基诞
HUANG Jidan(Glorious Sun School of Business and Management,Donghua University,Shanghai 200051,China)
出处
《实验室研究与探索》
CAS
北大核心
2020年第11期16-19,66,共5页
Research and Exploration In Laboratory
基金
国家自然科学基金项目(71771048,71872037,71571061)。
关键词
数值积分
灰狼优化算法
不等距点分割
单纯形法
numerical integral
gray wolves optimization(GWO)
inequality point segmentation
simplex method
