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基于网格的一种新的动态演化算法 被引量：2

New evolutionary algorithm based on girdding for dynamic optimization problems
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摘要 近年来,越来越多的演化计算研究者对动态优化问题产生了很大的兴趣,并产生了很多解决动态优化问题的方法。提出一种新的动态演化算法,与传统的演化算法有所不同,它是建立在划分网格基础上的,故而称它为网格优化算法。通过测试典型的动态优化问题,并与经典的SOS算法进行比较,证明了算法的有效性。 Dynamic Optimization Problems （DOP） has attracted more and more attention recently, and a number of methods have been introduced to solve them. This paper proposed a completely new approach, named Grid Evolutionary Algorithm （GEA）, which was based on the concept of Girdding and quite different from those traditional Evolutionary Algorithms （EAs）. It was designed to solve optimization problems in dynamic environment. Compared with the well-known SOS algorithms, the resuhs show that this algorithm is far more effective to solve DOP than SOS algorithm.
作者 于干 康立山
机构地区 中国地质大学计算机学院
出处 《计算机应用》 CSCD 北大核心 2008年第2期319-321,共3页 journal of Computer Applications
基金 国家自然科学基金资助项目(60473081) 湖北省自然科学基金资助项目(2005ABA234)
关键词 演化算法 动态优化 网格算法 有效性 evolutionary algorithms dynamic optimization grid algorithm effectiveness
分类号 TP393.01 [自动化与计算机技术—计算机应用技术] TP391 [自动化与计算机技术—计算机应用技术]
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参考文献13

  • 1BRANKE J,KAUFLER T,SCHMIDT C,et al.A multipopulation approach to dynamic optimization problems[C]// Adaptive Computing in Design and Manufacturing.New York:Springer-Verlag,2000:299-308.
  • 2GOLDBERG D E,SMITH R E.Non-stationary function optimization using genetic algorithms with dominance and diploidy[C]// Proceedings of the 2nd International Conference on Genetic Algorithms on Genetic algorithms and their application.NJ:Lawrence Erlbaum Associates,1987:59-68.
  • 3BRANKE J.Memory enhanced evolutionary algorithms for changing optimization problems[C]// Proceeding of the IEEE Congress on Evolutionary Computation.Washington D C:IEEE Service Center,1999:1875-1882.
  • 4于干,李长河,康立山.一种新的基于网格的函数优化算法[J].计算机应用,2007,27(7):1757-1759. 被引量：6
  • 5ZENG S Y,HE J.A novel evolutionary algorithm based on an orthogonal design for dynamic optimization problems[C]// Proceedings of the 2005 Congress on Evolutionary Computation.UK:IEEE Service Center,2005:1188-1195.
  • 6RAMAN N,TALBOT F B.The job shop tardiness problem:a decomposition approach[J].European Journal of Operational Research,1993,69(2):187-199.
  • 7BRANKE J.Evolutionary Optimization in Dynamic Environments[M].UK:Kluwer Academic Publishers,2002.
  • 8BRANKE J.Evolutionary approaches to dynamic optimization problems[C]// Proceedings of the Workshop on Evolutionary Algorithms for Dynamic Optimization Problems.Chicago:ACM,2003:1-3.
  • 9YANG S.Memory-based immigrants for genetic algorithms in dynamic environments[C]// Proceedings of the 2005 Genetic and Evolutionary Computation Conference.Washington D.C:ACM Press,2005:2105-2111.
  • 10RYAN C.Diploidy without dominance[C]// 3rd Nordic Workshop on Genetic Algorithms.Finland:Finnish Artificial Intelligence Society,1997:63-70.

二级参考文献11

  • 1YAO X,LIU Y.Fast evolutionary programming[Z].Computational intelligence group,School of Computer Science,University of New South Wales Australian Defence Force Academy,Canberra ACT,Australia,2006.
  • 2FOGEL L J,OWENS A J,WALSH M J.Artificial intelligence through stimulated evolution[M].New York:Wiley,1996.
  • 3LIM D,ONG Y S,JIN Y,et al.Trusted evolutionary algorithm[C]// IEEE Congress on Evolutionary Computation.Los Alamitos:IEEE Press,2006.
  • 4TEZUKA M,MUNETOMO M,AKAMA K,et al.Genetic algorithm to optimize fitness function with sampling error and its application to financial optimization problem[C]// IEEE Congress on Evolutionary Computation.Los Alamitos:IEEE Press,2006.
  • 5SEDGHI S,MASHHADI H R,KHADEMI M.Detecting hidden information from a spread spectrum watermarked signal by algorithm[C]// IEEE Congress on Evolutionary Computation.Los Alamitos:IEEE Press,2006.
  • 6KARAKASIS M K,GIOTIS A P,GIANNAKOGLOU K C.Inexact information aided,low-cost,distributed genetic algorithms for aerodynamic shape optimization[J].International Journal for Numerical Methods in Fluids,2003,43(10/11):1149-1166.
  • 7NISSEN V,PROPACH J.Optimization with noisy function evaluations[C]// Parallel Problem Sloving from Nature V.Berlin:Springer,1998.159-168.
  • 8BEKER T,HADANY L.Noise and elitism in evolutionary computation[C]// Soft Computing Systems-Design,Management and Applications,HIS2002.Amsterdam:IOS Press,2002,87:193-201.
  • 9FITZPATRICK J M,GREFENSETTE J J.Genetic algorithms in noisy environments[J].Machine Learning,1988,3(2/3):101-120.
  • 10BARTZ-BEIELSTEIN T.Experimental research in evolutionary computation[M].Berlin:Springer,2006.

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计算机应用
2008年 第2期

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