高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

正交免疫克隆粒子群多目标优化算法

丛琳 焦李成 沙宇恒

丛琳, 焦李成, 沙宇恒. 正交免疫克隆粒子群多目标优化算法[J]. 电子与信息学报, 2008, 30(10): 2320-2324. doi: 10.3724/SP.J.1146.2007.00566
引用本文: 丛琳, 焦李成, 沙宇恒. 正交免疫克隆粒子群多目标优化算法[J]. 电子与信息学报, 2008, 30(10): 2320-2324. doi: 10.3724/SP.J.1146.2007.00566
Cong Lin, Jiao Li-Cheng, Sha Yu-Heng. Orthogonal Immune Clone Particle Swarm Algorithm on Multiobjective Optimization[J]. Journal of Electronics and Information Technology, 2008, 30(10): 2320-2324. doi: 10.3724/SP.J.1146.2007.00566
Citation: Cong Lin, Jiao Li-Cheng, Sha Yu-Heng. Orthogonal Immune Clone Particle Swarm Algorithm on Multiobjective Optimization[J]. Journal of Electronics and Information Technology, 2008, 30(10): 2320-2324. doi: 10.3724/SP.J.1146.2007.00566

正交免疫克隆粒子群多目标优化算法

doi: 10.3724/SP.J.1146.2007.00566

Orthogonal Immune Clone Particle Swarm Algorithm on Multiobjective Optimization

  • 摘要: 该文基于抗体克隆选择学说理论,提出了一种求解多目标优化问题的粒子群算法正交免疫克隆粒子群算法(Orthogonal Immune Clone Particle Swarm Optimization, OICPSO)。根据多目标的特点,提出了适合粒子群算法的克隆算子,免疫基因算子,克隆选择算子。免疫基因操作中采用了离散正交交叉算子来获得目标空间解的均匀采样,得到理想的Pareto解集,并引入拥挤距离来减少获得Pareto解集的大小,同时获得具有良好均匀性和宽广性的Pareto最优解集。实验中,与NSGA-II和MOPSO算法进行了比较,并对算法的性能指标进行了分析。结果表明,OICPSO不仅增加了种群解的多样性而且可以得到分布均匀的Pareto有效解集,对于多目标优化问题是有效地。
  • Schaffer J D. Multiple objective optimization with vectorevaluated genetic algorithms[C]. In: the proceedings of theInternational Conference on Genetic Algorithms and TheirsApplications. Pittsburgh, PA, 1985: 93-100.[2]Fonseca C M and Fleming P J. Genetic algorithms formultiobjective optimization: formulation, discussion andgeneralization [C]. Proceedings of the Fifth InternationalConference on Genetic Algorithm (S. Forrest, ed.), California,university of Illinois at Urbana Champaign, MorganKaufman Publishers, 1993: 416-423.[3]Horn J and Nafpliotis N. Multiobjective optimization usingthe niched Pareto genetic algorithm. Illinois GeneticAlgorithms Laboratory, University of Illinois, Urbana,Champaign: IlliGAL Report 93005, 1993.[4]Srinivas N and Deb K. Multiobjective optimization usingnondominated sorting in genetic algorithms [C]. EvolutionaryComputation, 1994, 2(3): 221-248.[5]Zitzler E and Thiele L. Multiobjective evolutionaryalgorithms: A comparative case study and the strengthPareto approach [C]. IEEE Trans. on EvolutionaryComputation, 1999, 3(4): 257-271.[6]Zitzler E, Laumanns M, and Thiele L. SPEA2: Improving thestrength Pareto evolutionary algorithm. Swiss FederalInstitute of Technology, Lausanne, Switzerland, TechnicalReport TIK-Rep 103, 2001.[7]Deb K, Pratap A, Agarwal S, and Meyarivan T. A fast andelitist multiobjective genetic algorithm: NSGA-II [J].IEEETrans. on Evolutionary Computation.2002, 6(2):182-197[8]Coello C A and Lechuga M S. MOPSO: A proposal formultiple objective particle swarm optimization [C]. InProceedings of the Congress on Evolutionary Computation(CEC'2002)), Honolulu, Hawaii, USA 2002: 1051-1056.[9]杜海峰, 公茂果, 焦李成, 刘若辰. 用于高维函数优化的免疫记忆克隆规划算法[J]. 自然科学进展, 2004, 14(8): 925-933.Du H F, Gong M G, Jiao L C. Immune memory clonalprogramming algorithm for high-dimensional functionnumerical optimization [J]. Progress in Natural Science, 2004,8(14): 925-933.[10]Leung Y W and Wang Y. An orthogonal genetic algorithmwith quantization for global numerical optimization [J].IEEETrans. on Evolutionary Computation.2001, 5(1):41-53
  • 加载中
计量
  • 文章访问数:  3115
  • HTML全文浏览量:  44
  • PDF下载量:  843
  • 被引次数: 0
出版历程
  • 收稿日期:  2007-04-16
  • 修回日期:  2007-10-08
  • 刊出日期:  2008-10-19

目录

    /

    返回文章
    返回

    官方微信,欢迎关注