期刊文献+

进化算法的计算结果呈现方式

Presentation of computational results from evolutionary algorithms
下载PDF
导出
摘要 进化算法(Evolutionary Algorithms,EAs)作为求解非线性规划问题的有效求解工具已经越来越受到工程和优化领域的国内外专家和学者的重视,进化算法类的文章在世界上各种期刊中占据了大量比例。目前仍有很多刚刚从事进化算法理论与实践方面研究的国内学者对如何表现进化算法的计算结果比较迷茫。为此对于算法的计算结果展现方面进行了阐述。 In recent years,Evolutionary Algorithms(EAs)which are effective solving tools for non-linear programming problem are attracting more and more focuses of foreign and domestic specialists and scholars from engineering and optimization fields.Articles related with EAs take up a large proportion in various journals in the world.However,there are still many domestic scholars who are just engaging in research on the theoryapplications of EAs are in confusion about how to present the experimental results.This paper aims at providing those above-mentioned scholars and scientific research fellows with a reference and facilitating their research on EAs a little.
出处 《计算机工程与应用》 CSCD 2012年第5期35-36,50,共3页 Computer Engineering and Applications
基金 国家自然科学基金(No.70971017) 教育部人文社会科学研究项目(No.10YJC630009) 浙江省自然科学基金(No.Y1100854) 浙江省教育厅项目(No.Y201016979) 浙江省社科规划"之江青年课题研究"成果(No.11ZJQN064YB) 中国博士后科学基金(No.2011M500858)
关键词 进化算法 非线性规划 理论与实践 evolutionary algorithms non-linear programming theory&applications
  • 相关文献

参考文献3

二级参考文献22

  • 1曾三友,魏巍,康立山,姚书振.基于正交设计的多目标演化算法[J].计算机学报,2005,28(7):1153-1162. 被引量:36
  • 2雷德明,吴智铭.基于个体密集距离的多目标进化算法[J].计算机学报,2005,28(8):1320-1326. 被引量:23
  • 3郑金华,蒋浩,邝达,史忠植.用擂台赛法则构造多目标Pareto最优解集的方法[J].软件学报,2007,18(6):1287-1297. 被引量:54
  • 4Sierra M R, Coello C A C. Multi-objective particle swarm optimizers: A survey of the state-of-the-art[J]. Int J of Computational Intelligence Research, 2006, 2 (3) : 287-308.
  • 5Parsopoulos K E, Vrahatis M N. Particle swarm optimization in multiobjective problems[C]. Proc of the ACM 2002 Symposium on Applied Computing. Madrid, 2002: 603-607.
  • 6Parsopoulos K E, Tasoulis D K, Vrahatis M N. Multiobjective optimization using parallel vector evaluated particle swarm optimization [C]. Proc of the IASTED Int Conf on Artificial Intelligence and Applications, Innsbruck, 2004: 823-828.
  • 7Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Trans on Evolutionary Computation, 2002, 6 (2): 182-197.
  • 8Li X D. A non-dominated sorting particle swarm optimizer for multiobjeetive optimization [J]. Lecture Notes in Computer Science, 2003, 2723: 37-48.
  • 9Laumanns M, Thiele L, Deb K, et al. Combining convergence and diversity in evolutionary multi-objective optimization[J]. Evolutionary Computation, 2002, 10 (3) : 263-282.
  • 10Mostaghim S, Teich J. The role of ε-dominance in multi- objective particle swarm optimization methods[C]. Proc of IEEE Swarm Intelligence Symposium. Canberra, 2003: 1764-1771.

共引文献68

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部