Simplified Group Search Optimizer Algorithm for Large Scale Global Optimization

被引：0

作者：

张雯雰

机构：

[1] DepartmentofComputerScience,XiangnanUniversity

来源：

JournalofShanghaiJiaotongUniversity(Science) | 2015年 / 20卷 / 01期

关键词：

evolutionary algorithms; swarm intelli-gence; group search optimizer(PSO); large scale global optimization; function optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A simplified group search optimizer algorithm denoted as"SGSO"for large scale global optimization is presented in this paper to obtain a simple algorithm with superior performance on high-dimensional problems.The SGSO adopts an improved sharing strategy which shares information of not only the best member but also the other good members,and uses a simpler search method instead of searching by the head angle.Furthermore,the SGSO increases the percentage of scroungers to accelerate convergence speed.Compared with genetic algorithm(GA),particle swarm optimizer(PSO)and group search optimizer(GSO),SGSO is tested on seven benchmark functions with dimensions 30,100,500 and 1 000.It can be concluded that the SGSO has a remarkably superior performance to GA,PSO and GSO for large scale global optimization.

引用

收藏

页码：38 / 43

页数：6

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