An improved group search optimizer for mechanical design optimization problems

被引：39

作者：

Shen, Hai ^{[1
,2
,3
]}

Zhu, Yunlong ^{[1
]}

Niu, Ben ^{[1
,2
]}

Wu, Q. H. ^{[4
]}

机构：

[1] Chinese Acad Sci, Shenyang Inst Automat, Key Lab Ind Informat, Shenyang 110016, Peoples R China

[2] Chinese Acad Sci, Grad Sch, Beijing 100049, Peoples R China

[3] Shenyang Normal Univ, Coll Phys Sci & Technol, Shenyang 110034, Peoples R China

[4] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside, England

来源：

PROGRESS IN NATURAL SCIENCE-MATERIALS INTERNATIONAL | 2009年 / 19卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Mechanical optimization problem; GSO; Constrained optimization problem; PARTICLE SWARM OPTIMIZATION; EVOLUTIONARY; ALGORITHMS; SIMULATION;

D O I：

10.1016/j.pnsc.2008.06.007

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents an improved group search optimizer (iGSO) for solving mechanical design optimization problems. In the proposed algorithm, subpopulations and a co-operation evolutionary strategy were adopted to improve the global search capability and convergence performance. The iGSO is evaluated on two optimization problems of classical mechanical design: spring and pressure vessel. The experimental results are analyzed in comparison with those reported in the literatures. The results show that iGSO has much better convergence performance and is easier to implement in comparison with other existing evolutionary algorithms. (C) 2008 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.

引用

页码：91 / 97

页数：7

共 24 条

[1] A socio-behavioural simulation model for engineering design optimization [J].

Akhtar, S ;

Tai, K ;

Ray, T .

ENGINEERING OPTIMIZATION, 2002, 34 (04) :341-354

[2]

Arora J., 2004, INTRO OPTIMUM DESIGN

[3]

Belegundu A.D.:., 1982, A Study of Mathematical Programming Methods for Structural Optimization

[4]

Cao YJ, 1999, ENG INTELL SYST ELEC, V7, P77

[5] Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art [J].

Coello, CAC .

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2002, 191 (11-12) :1245-1287

[6] Constraint-handling in genetic algorithms through the use of dominance-based tournament selection [J].

Coello, CAC ;

Montes, EM .

ADVANCED ENGINEERING INFORMATICS, 2002, 16 (03) :193-203

[7] Use of a self-adaptive penalty approach for engineering optimization problems [J].

Coello, CAC .

COMPUTERS IN INDUSTRY, 2000, 41 (02) :113-127

[8]

GIVIGI SN, 2005, P IEEE INT C MECH AU, V2, P1005

[9] Fractal characteristic of silica xerogels with different additives [J].

He, Fei ;

He, Xiaodong ;

Li, Yao .

2006 1ST IEEE INTERNATIONAL CONFERENCE ON NANO/MICRO ENGINEERED AND MOLECULAR SYSTEMS, VOLS 1-3, 2006, :1272-+

[10] An effective co-evolutionary particle swarm optimization for constrained engineering design problems [J].

He, Qie ;

Wang, Ling .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2007, 20 (01) :89-99

← 1 2 3 →