不好意思，附件里面加图，好像一直不行，请版主帮忙一下

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Originally posted by nagtive at 2010-02-10 14:20:00:
不好意思，附件里面加图，好像一直不行，请版主帮忙一下

把图发来，或者你就直接利用上传功能来发。

无论如何，你的文章是会议的文章，还是按CA算最保险，也最合理。。。

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Originally posted by nagtive at 2010-02-10 14:09:13:
KEY ENGINEERING MATERIALS 确实是CA收录的
如下图


[ Last edited by nagtive on 2010-2-10 at 22:55 ]

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
，

楼上查的链接是清华镜像，不是国际出口！所以显示是JA。

引用回帖:

Originally posted by danshw at 2010-02-10 23:52:59:
楼上查的链接是清华镜像，不是国际出口！所以显示是JA。

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