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极值优化算法研究 被引量:3

Research on Extremal Optimization Algorithm
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摘要 极值优化算法是一种新的、通用的启发式优化方法。其基本思想是更新适值最差的变量,提高目标函数的适值。极值优化算法(EO)由于它的简单易于实现和强大的功能,已受到学术界的广泛关注。介绍了基本的EO算法、若干类改进的EO算法及其原理和应用,并讨论将来可能的研究内容。 Extreme optimization is a new general-purpose heuristic optimization approach . Its principle is to select and update the worst variable, and improve the fitness of the whole problem. Extreme optimization algorithm has become the hotspot of evolutionary computation because of its excellent performance and simple for implement. In this paper, classical extreme optimization algorithm and its several variants and its mechanism and some applications of the algorithm are introduced, future research issues are also discussed.
出处 《武汉理工大学学报》 CAS CSCD 北大核心 2009年第3期40-44,共5页 Journal of Wuhan University of Technology
基金 国家自然科学基金(40701153)
关键词 极值优化算法 自组织临界 组合优化 extreme optimization algorithm self-organized criticality punctuated equilibrium evolutionary computation
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参考文献20

  • 1Boettcher S, Percus A G. Extremal Optimization: Methods Derived from Co-Evolution[ C]. Proceedings of t.he Genetic and Evolutionary Computation Conference[A]. San Francisco: Morgan Kaufmann, 1999:825-832.
  • 2Boettcher S, Percus A G. Nature' s Way of Optimizing[ J ]. Artificial Intelligence, 2000, 119 (1/2) : 275-286.
  • 3Bak P, Tang C, Wiesenfeld K. Self-Organized Criticality: An Explanation of the 1/f Noise [J ]. Physical Review Letters, 1987,59(4): 381-384.
  • 4Bak P, Sneppen K. Punctuated Equilibrium and Criticality in a Simple Model of Evolution[ J ]. Physical Review Letters, 1993, 71 (24) : 4083-4086.
  • 5Boettcher S, Percus A G, Grigni M. Optimizing Through Co-evolutionary Avalanches[J]. Lecture Notes in Computer Science, 2000,1917(4) :447-456.
  • 6Boettcher S. Extremal Optimization: Heuristics Via Co-evolutionary Avalanches[J]. Computing in Science and Engineering, 2000,6 (2) : 75 -82.
  • 7Boettcher S, Percus A G. Combining Local Search with Co-evolution in a Remarkably Simple Way[C] . Proc of the 2000 Congress on Evolutionary Computation USA[A]. 2000:1578-1584.
  • 8Boettcher S, Percus A G. Optimization with Extremal Dynarnics[J]. Complexity, 2003,8(2):57-62.
  • 9Boettcher S, Percus A G. Extremal Optimization for Graph Partitioning[ J ]. Physical Review E, 2001,64 (2) : 1-13.
  • 10Sousa F L, Ramos F M. Function Optimization Using Extremal Dynamics[C]. 4th Int Conf on Inverse Problems in Engineering- Theory and Practice Brazil[ A]. 2002 : 115-119.

同被引文献20

  • 1梁慧勇,顾幸生.改进适应值函数的遗传算法[J].中南大学学报(自然科学版),2003,34(z1):72-75. 被引量:1
  • 2李建华,王孙安,杜海峰.一种改进的遗传算法:Family GA[J].控制与决策,2004,19(9):999-1003. 被引量:12
  • 3段亚南,何霆,褚滨生.基于自适应混合启发算法求解一类JSP问题[J].计算机工程与设计,2004,25(7):1206-1207. 被引量:5
  • 4张超勇,饶运清,刘向军,李培根.基于POX交叉的遗传算法求解Job-Shop调度问题[J].中国机械工程,2004,15(23):2149-2153. 被引量:110
  • 5MacQUEEN J B. Some methods for classification and analysis of mult- ivariate observations [ C ]//Proc of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley : University of Cali- fomia Press, 1967:281 - 297.
  • 6EISEN M B, SPELLMAN P T, BROWN P O, et al. Cluster analysis and display of genome-wide expression patterns[ J]. Proceedings of the National Academy of Sciences, 1998,95(25 ) :14863-14868.
  • 7KOHONEN T. The self-organizing maps [ J ]. Proceedings of the IEEE, 1990,78 (9) : 1464-1480.
  • 8DU Zhi-hua,WANG Yi-wei, JI Zhen. PK-means: a new algorithm for gene clustering [ J ]. Computational Biology and Chemistry,2008, 32(4) :243-247.
  • 9BOETTCHER S, PERCUS A G. Extremal optimization: methods de- rived from co-evolution [ C ]//Proc of Genetic and Evolutionary Compu- tation Conference. San Francisco: Morgan Kaufmann, 1999:825- 832.
  • 10CHEN Min-rong, LI Xia, ZHANG Xi, et al. A novel particle swarm optimizer hybridized with extremal optimization [ J ]. Applied Soft Computing, 2010, 10 ( 2 ) : 367- 373.

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