Dynamic Multi-group Self-adaptive Differential Evolution Algorithm with Local Search for Function Optimization

ZHANG Xue-xia; CHEN Wei-rong; DAI Chao-hua

您当前的位置：

首页 >

文章列表页 >

Dynamic Multi-group Self-adaptive Differential Evolution Algorithm with Local Search for Function Optimization

更新时间：2025-07-16

- Dynamic Multi-group Self-adaptive Differential Evolution Algorithm with Local Search for Function Optimization
- Acta Electronica Sinica Vol. 38, Issue 8, Pages: 1825-1830(2010)
- 作者机构：
  
  西南交通大学电气工程学院,四川,成都,610031
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：2010
- 稿件说明：
移动端阅览
ZHANG Xue-xia, CHEN Wei-rong, DAI Chao-hua. Dynamic Multi-group Self-adaptive Differential Evolution Algorithm with Local Search for Function Optimization[J]. Acta Electronica Sinica, 2010, 38(8): 1825-1830.
DOI：

ZHANG Xue-xia, CHEN Wei-rong, DAI Chao-hua. Dynamic Multi-group Self-adaptive Differential Evolution Algorithm with Local Search for Function Optimization[J]. Acta Electronica Sinica, 2010, 38(8): 1825-1830. DOI：

摘要

提出将一种改进的差分进化算法——带局部搜索的动态多群体自适应差分进化算法(DMSDELS)应用于函数优化.该算法将种群中的个体随机动态分成多个子群体

以增强个体间的信息交换;变异操作中

选择最优个体为基向量

差分向量的方向选择有利于搜索的方向

以提高收敛速度;变异尺度因子F与交叉概率CR采用自适应机制

以平衡局部搜索与全局搜索;部分优秀个体搜索达到指定代数进入局部搜索

以加快收敛.通过对13个benchmark典型复杂函数进行测试

并与其他七种优化算法进行比较

仿真结果表明:DMSDELS算法具有较高的搜索精度和收敛性

且具有较强的跳出局部最优解能力.

Abstract

An improved algorithm based on differential evolution algorithms

dynamic multi-group self-adaptive differential evolution algorithm with local search (DMSDELS)

is applied to optimize functions in this paper.In DMSDELS

the population is randomly and dynamically divided into multi-group individuals

which can exchange information.To speed up search

in the mutation phase the best individual is chosen as the base vector

and the selection of the direction for difference vector is benefit to search.The scaling factor F and the crossover rate CR are self-adapted in order to balance the local search and the global search.To accelerate the convergence

elitist individuals could search in local after they explored specified generations.DMSDELS is tested on thirteen complex benchmark functions.The results are compared with those of other seven algorithms.The results show that DMSDELS is better in the search precision

convergence property and has strong ability to escape from the local sub-optima.

关键词

Keywords

references

Views

1909

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Federated Learning Scheme for Environmental Monitoring Based on Edge Computing

A Method of Security Situation Prediction for Industrial Internet Based on Optimized Support Vector Regression

Estimation Method of Wireless Link Quality and Routing Optimization Algorithm Based on Correlation Transmission Model

Speed Regulation of Scale Adjustment in Quantum-Inspired Optimization Algorithm

Two-Stage Localization Algorithm Based on Greedy Idea for Wireless Sensor Networks

Related Author

WANG Hai-juan

CHEN Yi-lin

ZHOU Wei

WU Yu-ting

HAN Yu-qing

JIANG Wei-jin

HU Xiang-dong

LÜ Gao-fei

Related Institution

College of Frontier Intersection， Hunan University of Technology and Business

Xiangjiang Laboratory

School of Computer Science and Artificial Intelligence， Wuhan University of Technology

School of Computer Science， Hunan University of Technology and Business

School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications

⁰