期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

一种量化正交免疫克隆粒子群数值优化算法 被引量：2

An Orthogonal Immune Clone Particle Swarm Algorithm with Quantization for Numerical Optimization
原文传递
导出
摘要 为了解决粒子群算法收敛速度慢和早熟收敛等问题,根据生物免疫系统理论中的克隆选择学说,提出一种量化正交免疫克隆粒子群算法.给出正交子空间分割算法,并采用正交交叉策略来增强子代个体解分布的均匀性.为避免个体邻域内最优解的丢失,提出一种自学习算子.并证明该算法的全局收敛性.实验中对标准测试函数进行20～1000维的测试,分别与5种算法进行比较,并给出算法参数对计算复杂度的影响.结果表明,本文方法有效克服早熟收敛,并且在保持种群多样性的同时提高收敛速度. In order to overcome prematurity and low searching speed of PSO algorithm, an orthogonal immune clone particle swarm algorithm with quantization （OICPSO/Q） is proposed according to the immune clone selection theory. An orthogonal subspace division method is presented and the orthogonal crossover strategy is used to increase the uniformity of solution. To avoid losing the optimal solution in neighborhood of individuals, a self-learning operator is presented. The global convergence of OICPSO/Q has been proved by theoretical analysis. In experiments, OICPSO/Q is tested on unconstrained benchmark problems with 20-1 000 dimensions, and is compared with five methods. The effects of parameters on computational cost of the algorithm are analyzed. The results indicate that OICPSO/Q is capable of solving complex problems and preserving the diversity of population. To some extent, it avoids prematurity and improves the convergence speed.
作者 丛琳 焦李成 沙宇恒
机构地区 西安电子科技大学智能信息处理研究所
出处 《模式识别与人工智能》 EI CSCD 北大核心 2007年第5期583-592,共10页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金项目(No.60133010.No.60372045) 国家863计划项目(No.2002AA135080) 国家973计划项目(No.2001CB309403)资助
关键词 粒子群优化 人工免疫系统 克隆选择 正交设计 进化计算 Particle Swarm Optimization, Artificial Immune System, Clone Selection,Orthogonal Design, Evolutionary Computation
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献14

  • 1Kennedy J, Eberhart R C. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Perth, Australia, 1995, IV: 1942-1948.
  • 2Shi Yuhui, Eberhart R C. Fuzzy Adaptive Particle Swarm Optimization // Proc of the IEEE Congress on Evolutionary Computation. Seoul. Korea, 2001: 101-106.
  • 3van den Bergh F, Engelbrecht A P. A Cooperative Approach to Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 225-239.
  • 4Kennedy J, Mendes R. Population Structure and Particle Swarm Performance // Proc of the IEEE Congress on Evolutionary Computation. Honolulu, USA, 2002:1671-1676.
  • 5黄艳新,周春光,邹淑雪,王岩.一种求解类覆盖问题的混合算法[J].软件学报,2005,16(4):513-522. 被引量：14
  • 6Xie X F, Zhang W J, Yang Z L. A Dissipative Particle Swarm Optimization // Proc of the IEEE Congress on Evolutionary Computation. Honolulu, USA, 2002:1456-1461.
  • 7Higashi N, Iba H. Particle Swarm Optimization with Gaussian Mutation // Proc of the IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, 2003:72-79.
  • 8Kennedy J. Bare Bones Particle Swarms // Proc of the IEEE Swarm Intelligence Symposium; Indianapolis, Indiana, 2003: 80-87.
  • 9Ramaweera A, Halgamuge S K, Waston H C. Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Trans on Evolutionary Computation, 2004, 8(3): 240-255.
  • 10Yao Xin, Liu Yong, Lin Guangming. Evolutionary Programming Made Faster. IEEE Trans on Evolutionary Computation, 1999, 3(2): 82-102.

二级参考文献12

  • 1HUANGYanxin ZHOUChunguang.Recognizing the Taste Signals Using the Clustering-Based Fuzzy Neural Network[J].Chinese Journal of Electronics,2005,14(1):21-25. 被引量：1
  • 2[9]陆德源,等.现代免疫学.上海:上海科技教育出版社,1998
  • 3[10]Muhlenbein H, et al. Predictive models for the breeder genetic algorithm. Evolutionary Computation, 1993, 1 (1): 25
  • 4[11]Leung Y W, et al. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation, 2001, 5(1): 41
  • 5[1]Dasgupta D, et al. Artificial immune systems in industrial applications. In: IPMM′99. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials.IEEE Press, 1999. 257～267
  • 6[3]Cooper K D, et al. Procedure cloning. In: Proceedings of the 1992International Conference on Computer Languages. IEEE Press,1992. 96～ 105
  • 7[4]BalazinskA M, et al. Advanced clone-analysis to support object-oriented system refactoring. In: Proceedings: Seventh Working Conference on Reverse Engineering, IEEE Press, 2000. 98
  • 8[5]Esmaili N, et al. Behavioural cloning in control of a dynamic system. In: IEEE International Conference on Systems, Man and Cybernetics Intelligent Systems for the 21st Century. 1995, 3:2904
  • 9[6]Hybinette M, et al. Cloning: A novel method for interactive parallel simulation. In: Proceedings of the 1997 Winter Simulation Conference. IEEE Press, 1997. 444
  • 10[7]Castro L N De, et al. Learning and Optimization using the clonal selection principle. IEEE Trans Evolutionary Computation, Special Issue on Artifical Immune Systems, 2002, 6(3): 239

共引文献31

同被引文献19

  • 1奚玮君,李绍军,钱锋.基于Alopex的粒子群算法及其在软测量上的应用[J].计算机与应用化学,2006,23(11):1045-1048. 被引量：7
  • 2KENNEDY J,EBERHART R C.Particle swarm optimization[C]//Proc of IEEE International Conference on Neural Networks.1995:1942-1948.
  • 3COELLO C A C, PULIDO G T,LECHUGA M S.Handling multiple objectives with particle swarm optimization[J].IEEE Trans on Evolutionary Computation,2004,8(3):256-279.
  • 4MOSTAGHIM S,TEICH J.Strategies for finding good local guides in multi-objective particle swarm optimization(MOPSO)[C]//Proc of IEEE Swarm Intelligence Symposium.2003:26-33.
  • 5SIERRA M R,COELLO C A C.Improving PSO-based multi-objective optimization using crowding, mutation and ε-dominance[C]//Proc of the 3rd International Conference on Evolutionary Multi-Criterion Optimization.2005:505-519.
  • 6DEB K,PRATAP A,AGRWAL S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ[J].IEEE Trans on Evolutionary Computation,2002,6(2):182-197.
  • 7LEUNG Y W,WANG Y.An orthogonal genetic algorithm with quantization for global numerical optimization[J].IEEE Trans on Evolutionary Computation,2001,5(1):41-53.
  • 8ZILTLER E.Evolutionary algorithms for multiobjective optimization:methods and applications[D].Zurich:Swiss Federal Inst Technol,1999.
  • 9Alejandro Bia. Alopex-B: A new, simpler, but yet faster ver- sion of the alopex training algorithm [ J ]. International Jour- nal of Neural Systems ,2001,11 (6) : 497 - 507.
  • 10Kennedy J, Eberhart R C. Particle swarm optimization [ C ]. IEEE International Conference on Neural Networks, Perth, Australia: IEEE Press, 1995 : 1942 - 1948.

引证文献2

二级引证文献7

模式识别与人工智能
2007年 第5期

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