Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (17): 48-51.DOI: 10.3778/j.issn.1002-8331.2010.17.014

• 研究、探讨 • Previous Articles     Next Articles

Improving performance of evolutionary algorithm by adding elite solutions to population during evolution process——Application to deal with flexible job-shop scheduling problem

LIU Xin-chuang1,QIU Hong-ze2,WEI Er-you3,SU Zhao-feng3   

  1. 1.Finance Division of Qufu Normal University,Qufu,Shandong 273165,China
    2.Computer Science and Technology Academy,Shandong University,Jinan 250061,China
    3.Management School of Ludong University,Yantai,Shandong 264025,China
  • Received:2008-12-18 Revised:2009-02-23 Online:2010-06-11 Published:2010-06-11
  • Contact: LIU Xin-chuang



  1. 1.曲阜师范大学 财务处,山东 曲阜 273165
    2.山东大学 计算机学院,济南 250061
    3.鲁东大学 管理学院,山东 烟台 264025
  • 通讯作者: 刘新闯

Abstract: Deficiency of population diversity always leads to premature convergence,which deeply limits performance of evolutionary algorithm.Solution quality can not be improved by simply enlarging search scale.To improve performance of evolutionary algorithm,part individuals in population are replaced with better solutions.The operation repeats for many times during evolution process according to search scale.The proposed strategy is studied on the basis of a symbiotic evolutionary algorithm which is used for dealing with complex flexible job-shop scheduling problem.Results of extensive computational simulations show that the proposed strategy shows better performance.Compared with traditional evolutionary algorithm,new proposed strategy shows higher efficiency in getting better solutions no matter whether search scale is large or not.

Key words: evolutionary algorithm, premature convergence, job shop scheduling problem

摘要: 进化算法进化过程中种群多样性的降低导致的收敛极大限制了进化算法的求解质量与搜索效率,通过扩大搜索规模并不能有效提高算法求解质量。在共生进化算法求解柔性作业调度的基础上,研究进化算法在较大搜索规模下的种群状态进化过程,并在进化过程向种群内批量加入优势元素,调整种群模式构成。仿真实验表明:与传统进化算法相比,进化过程中加入优势元素能有效提高算法的求解质量与搜索效率,在较短的时间内能得到较好的解,并且在较大搜索规模时表现了更好的搜索性能。

关键词: 进化算法, 早熟收敛, 作业调度

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