Grasshopper optimization algorithm with nonlinear weight and cauchy mutation

WANG Tao

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Grasshopper optimization algorithm with nonlinear weight and cauchy mutation

更新时间：2026-09-04

- Grasshopper optimization algorithm with nonlinear weight and cauchy mutation
- Vol. 37, Issue 5, Pages: 82-86(2020)
- 作者机构：
  
  1. 中国科学院机构国家天文台南京天文光学技术研究所,江苏,南京,210042
  2. 中国科学院天文光学技术重点实验室(南京天文光学技术研究所),江苏,南京,210042
- 作者简介：
- 基金信息：
- DOI：
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- Published：2020
- 稿件说明：
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王涛. 非线性权重和柯西变异的蝗虫算法[J]. 微电子学与计算机, 2020, 37(5): 82-86.

WANG Tao. Grasshopper optimization algorithm with nonlinear weight and cauchy mutation[J]. 2020, 37(5): 82-86.
王涛. 非线性权重和柯西变异的蝗虫算法[J]. 微电子学与计算机, 2020, 37(5): 82-86. DOI：

WANG Tao. Grasshopper optimization algorithm with nonlinear weight and cauchy mutation[J]. 2020, 37(5): 82-86. DOI：

摘要

针对蝗虫优化算法收敛速度慢，易陷入局部最优等问题，提出一种基于非线性权重和柯西变异的蝗虫优化算法.首先使用佳点集初始化种群，均匀种群分布.然后将线性权重改进为非线性权重，提高勘探和开发能力.同时对最优个体采用柯西变异，增加跳出局部最优能力.最后通过6个基准函数进行测试，实验表明改进后蝗虫算法收敛速度快、精度高.

Abstract

To overcome the problems of slow convergence

falling into local optima of the standard grasshopper optimization algorithm

a nonlinear weight and cauchy mutation in grasshopperoptimization algorithm is proposed. Firstly

the good point set is applied to initial population and uniform population distribution.Then the linearlyweightis improved to a nonlinear weight toenhance the exploration and exploitation capabilities of thegrasshopper optimization algorithm.At the same time

the cauchy mutation is added to the best grasshopper to increase the ability to jump out of local optima.Finally

six benchmark functions are selected for testing. Experiments show that the improved algorithm has fast convergence and high precision.

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references

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郭振洲, 王平, 马云峰, 等.基于自适应权重和柯西变异的鲸鱼优化算法[J].微电子学与计算机, 2017, 34(9):20-25. DOI: 20

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