中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
An improved group search optimizer for mechanical design optimization problems

文献类型:期刊论文

作者Zhu YL(朱云龙)
刊名Progress in Natural Science
出版日期2009
卷号19期号:1页码:91-97
关键词Mechanical optimization problem GSO Constrained optimization problem
ISSN号1002-0071
产权排序1
中文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Materials Science, Multidisciplinary ; Multidisciplinary Sciences
研究领域[WOS]Materials Science ; Science & Technology - Other Topics
关键词[WOS]PARTICLE SWARM OPTIMIZATION ; GENETIC ALGORITHMS ; EVOLUTIONARY ; SIMULATION
收录类别SCI
资助信息supported by the National Natural Science Foundation of China (Grant No.70431003);; the High-Tech Research and Development Program of China (Grant No.20060104A1118);; the Science and Technology Supporting Program of China (Grant No.2006BAH02A09);; the Abroad Preeminence Scholar Fund of Chinese Academy of Sciences (Grant No.2006-1-8)
语种英语
WOS记录号WOS:000261837000012
公开日期2012-05-29
源URL[http://ir.sia.ac.cn/handle/173321/7062]  
专题沈阳自动化研究所_工业信息学研究室
推荐引用方式
GB/T 7714
Zhu YL. An improved group search optimizer for mechanical design optimization problems[J]. Progress in Natural Science,2009,19(1):91-97.
APA Zhu YL.(2009).An improved group search optimizer for mechanical design optimization problems.Progress in Natural Science,19(1),91-97.
MLA Zhu YL."An improved group search optimizer for mechanical design optimization problems".Progress in Natural Science 19.1(2009):91-97.

入库方式: OAI收割

来源:沈阳自动化研究所

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