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KEY ENGINEERING MATERIALS 确实是CA收录的 已有4人参与
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KEY ENGINEERING MATERIALS 确实是CA收录的 如下图  [ Last edited by nagtive on 2010-2-10 at 22:55 ] |
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2楼2010-02-10 14:20:00
xiaogounihao
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3楼2010-02-10 14:54:36
4楼2010-02-10 14:59:56
visitor958
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5楼2010-02-10 15:43:25
stoutgun
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nicer(金币+1): 2010-02-11 16:23
小木虫(金币+0.5):给个红包，谢谢回帖交流
nicer(金币+1): 2010-02-11 16:23
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我查到的还是JA，如下： 1. An immune particle swarm optimization algorithm for solving permutation flowshop problem Qiu, Chang-Hua (College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China); Wang, Can Source: Key Engineering Materials, v 419-420, p 133-136, 2010 Language: English Database: Compendex Abstract - Detailed - Full-text 进入Detailed，如下： 1. Accession number: 20094512426482 Title: An immune particle swarm optimization algorithm for solving permutation flowshop problem Authors: Qiu, Chang-Hua1, 2 ; Wang, Can1 Author affiliation: 1 College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China 2 Heilongjiang Modern Manufacturing Engineering Research Center, Harbin 150001, China Corresponding author: Qiu, C.-H. (qiuchanghua@hrbeu.edu.cn) Source title: Key Engineering Materials Abbreviated source title: Key Eng Mat Volume: 419-420 Issue date: 2010 Publication year: 2010 Pages: 133-136 Language: English ISSN: 10139826 CODEN: KEMAEY Document type: Journal article (JA) Publisher: Trans Tech Publications Ltd, Laubisrutistr.24, Stafa-Zuerich, CH-8712, Switzerland Abstract: To solve the permutation flowshop problem more effectively, a novel artificial immune particle swarm optimization (PSO) algorithm has been proposed. The new algorithm combined the biology immune system theory with particle swarm algorithm by the following phases. Firstly, the scheduling objective and constrain condition were served as antibodies while solutions was served as antigens. Secondly, the particles were encoded as workpiece processing sequence. Furthermore, a concentration selection strategy was adopted to maintain the particle diversity. Finally, comparing with genetic algorithm and PSO, case results showed that immune PSO algorithm not only optimized results and convergence velocity but also had a small fluctuation. Number of references: 5 Main heading: Particle swarm optimization (PSO) Controlled terms: Algorithms - Antigens - Biology - Convergence of numerical methods - Machine shop practice Uncontrolled terms: Artificial immune - Constrain condition - Convergence velocity - Immune algorithm - Immune particle swarm optimization - Immune PSO - Immune systems - Particle swarm algorithm - Permutation flow shop - Permutation flow shops - Small fluctuation - Work pieces Classification code: 921.6 Numerical Methods - 921.5 Optimization Techniques - 921 Mathematics - 723 Computer Software, Data Handling and Applications - 604.2 Machining Operations - 461.9.1 Immunology - 461.9 Biology DOI: 10.4028/www.scientific.net/KEM.419-420.133 Database: Compendex Compilation and indexing terms, © 2009 Elsevier Inc. |
6楼2010-02-10 23:32:49
danshw
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7楼2010-02-10 23:52:59
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xiaogounihao(金币+1):谢谢提供 2010-02-11 10:37
小木虫(金币+0.5):给个红包，谢谢回帖交流
xiaogounihao(金币+1):谢谢提供 2010-02-11 10:37
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国际出口的,如下: Accession number: 20094512426482 Title: An immune particle swarm optimization algorithm for solving permutation flowshop problem Authors: Qiu, Chang-Hua1, 2 ; Wang, Can1 Author affiliation: 1 College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China 2 Heilongjiang Modern Manufacturing Engineering Research Center, Harbin 150001, China Corresponding author: Qiu, C.-H. (qiuchanghua@hrbeu.edu.cn) Source title: Key Engineering Materials Abbreviated source title: Key Eng Mat Volume: 419-420 Issue date: 2010 Publication year: 2010 Pages: 133-136 Language: English ISSN: 10139826 CODEN: KEMAEY Document type: Conference article (CA) Publisher: Trans Tech Publications Ltd, Laubisrutistr.24, Stafa-Zuerich, CH-8712, Switzerland Abstract: To solve the permutation flowshop problem more effectively, a novel artificial immune particle swarm optimization (PSO) algorithm has been proposed. The new algorithm combined the biology immune system theory with particle swarm algorithm by the following phases. Firstly, the scheduling objective and constrain condition were served as antibodies while solutions was served as antigens. Secondly, the particles were encoded as workpiece processing sequence. Furthermore, a concentration selection strategy was adopted to maintain the particle diversity. Finally, comparing with genetic algorithm and PSO, case results showed that immune PSO algorithm not only optimized results and convergence velocity but also had a small fluctuation. Number of references: 5 Main heading: Particle swarm optimization (PSO) Controlled terms: Algorithms - Antigens - Biology - Convergence of numerical methods - Machine shop practice Uncontrolled terms: Artificial immune - Constrain condition - Convergence velocity - Immune algorithm - Immune particle swarm optimization - Immune PSO - Immune systems - Particle swarm algorithm - Permutation flow shop - Permutation flow shops - Small fluctuation - Work pieces Classification code: 921.6 Numerical Methods - 921.5 Optimization Techniques - 921 Mathematics - 723 Computer Software, Data Handling and Applications - 604.2 Machining Operations - 461.9.1 Immunology - 461.9 Biology DOI: 10.4028/www.scientific.net/KEM.419-420.133 Database: Compendex Compilation and indexing terms, © 2009 Elsevier Inc |
8楼2010-02-11 06:31:58
9楼2010-02-11 10:22:34
wpq113 
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10楼2010-02-11 16:19:25
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