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Solving Job-shop Scheduling Problem based on Improved Adaptive Particle Swarm Optimization Algorithm
  • ISSN号：1005-1120
  • 期刊名称：Transactions of Nanjing University of Aeronautics
  • 时间：2014.6.1
  • 页码：275-293
  • 分类：TH165[机械工程—机械制造及自动化]
  • 作者机构：[1]College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing, 210016, P.R. China, [2]College of Mechanical and Electrical Engineering,Hohai University Changzhou, Changzhou, 213022, P.R. China
  • 相关基金：Supported by the National Natural Science Foundation of China （51175262）; the Research Fund for Doctoral Program of Higher Education of China （20093218110020） ; the Jiangsu Province Science Foundation for Excellent Youths （BK201210111） ; the Jiangsu Province Industry-Academy-Research Grant （BY201220116） ; the Innovative and Ex- cellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics （BCXJ10-09）.
  • 相关项目：基于神经-体液调控机制的有机制造系统自适应控制研究
作者： Wenbin Gu|Dunbing Tang|Kun Zheng|
中文摘要：

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

英文摘要：

An improved adaptive particle swarm optimization （IAPSO） algorithm is presented for solving the mini- mum makespan problem of job shop scheduling problem （JSP）. Inspired by hormone modulation mechanism, an adaptive hormonal factor （HF）, composed of an adaptive local hormonal factor （ Hi ） and an adaptive global hor- monal factor （H~）, is devised to strengthen the information connection between particles. Using HF, each particle of the swarm can adjust its position self adaptively to avoid premature phenomena and reach better solution. The computational results validate the effectiveness and stability of the proposed IAPSO, which can not only find opti- mal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization （PSO） algorithms.

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期刊信息
  • 《南京航空航天大学学报：英文版》
  • 主管单位:工业和信息化部
  • 主办单位:南京航空航天大学
  • 主编：宣益民
  • 地址：南京市御道街29号1016信箱
  • 邮编：210016
  • 邮箱：Tnuaa@nuaa.edu.cn
  • 电话：025-84892071
  • 国际标准刊号：ISSN：1005-1120
  • 国内统一刊号：ISSN：32-1389/V
  • 邮发代号:
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  • 被引量:205