期刊文献+

量子粒子群算法求解整数规划的方法 被引量:8

Solving Integer Programming Based on Quantum Particle Swarm Optimization
下载PDF
导出
摘要 粒子群算法主要用于优化连续性问题。如果用于求解整数规划问题,算法的粒子位置必须解决取整问题;而量子粒子群算法求解整数规划问题具有更高的效率。利用三种取整方法与量子粒子群算法结合,求解非线性整数规划问题,并且与标准粒子群算法求解整数规划问题进行比较。通过对基准函数仿真实验,比较了六种方法求解整数规划问题。实验结果表明,基于随机取整的量子粒子群算法搜索成功率优于其他五种方法,其综合搜索效率更佳。寻找了一种更优的求解整数规划方法。 The standard Particle Swarm Optimization mainly be used to optimize continuous problem.If using to solve integer programming,the position of particle must be rounded.However,it is more efficient for Quantum Particle Swarm Optimization(QPSO) to solve integer programming.Three kinds of rounding location of particle for QPSO are used to optimize the integer programming,and compared to the standard PSO with the same three kinds of rounding location of particle.By the simulation on benchmark functions,six kind of solving integer programming are compared with.The results of experiment show that the QPSO based on random rounding outperforms the other ways and its' searching efficiency is higher than that of the other ways,so a better method of solving integers programming is found.
出处 《科学技术与工程》 2011年第33期8195-8198,8202,共5页 Science Technology and Engineering
基金 福建省教育厅科技项目(JK2011035)资助
关键词 量子粒子群 整数规划 随机取整 优化算法 QPSO integer programming random rounding optimization algorithm
  • 相关文献

参考文献6

  • 1Kennedy J, Eberhart R. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. Piscataway: 1995 : 1942-1948.
  • 2Shi Y, Eherhart R C. A modified particle swarm optimization. Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, 1998:69-73.
  • 3Clerc M. The swarm and the queen : Towards a deterministic and adaptive particle swarm optimization. Proceedings of the Congress of Evolutionary Computation ,Washington, 1999 : 1951-1957.
  • 4Sun Jun, Xu Fengbin, Xu Wenbo. Particle swarm optimization with particles having quantum behavior. Proc of Congress on Evolutionary Computation, USA,2004:325-331.
  • 5刘静,须文波,孙俊.基于量子粒子群算法求解整数规划[J].计算机应用研究,2007,24(3):79-81. 被引量:17
  • 6贺益君,陈德钊.适于混合整数非线性规划的混合粒子群优化算法[J].浙江大学学报(工学版),2008,42(5):747-751. 被引量:12

二级参考文献21

  • 1GALL D A. A practical muhifactor optlmization criterion[ M]//LAUI A, UOGEL T P. Recent advances in optimization techniques. New York : John-Wiley, 1966:369- 386.
  • 2RUDOLPH G. An evolutionary algorithm for integer programming[ M ]//Parallel problem solving from nature-PPSN I. Berlin: Springer, 1994 : 139-148.
  • 3KENNEDY J, EBERHART R C. Particle swarm optimization : proc.of IEEE Int. Conf. on Neural Networks [ C ]. Piscataway : [ s. n. ],1995 : 1942-1948.
  • 4SHI Y, EBERHART R C. A modified particle swarm optimizer:proc.of the IEEE Conference on Evolutionary Computation [ C ]. Anchorage: [ s. n. ] ,1998:69-73.
  • 5CLERC M, KENNEDY J. The particle swarm:explosion, stability and convergence in a multi-dimensional complex space [ J ]. IEEE Transactions on Evolutionary Computation, 2002,6( 1 ) :58-73.
  • 6BERGH F V, ENGELBRECHT A P. A new locally convergent particle swarm optimizer:IEEE International Conference on Systems, Man and Cybernetics [ C ]. Tunisia: [ s. n. ],2002:94-99.
  • 7SUN J, FENG B, XU W. Particle swarm optimization with particles having quantum behavior:IEEE Congress on Evolutionary Computation[C]. USA:[s. n. ] ,2004:325-331.
  • 8SUN J, XU W. A global search strategy of quantum-behaved particle swarm optimization : IEEE Conference on Cybernetics and Intelligent Systems [ C ]. Singapore : [ s. n. ], 2004 : 111 - 116.
  • 9PARSOPOULOS K E, VRAHATIS M N. Recent approaches to global boptimization problems through particle swarm optimization [ J ]. Natural Computing,2002,1 ( 2 - 3 ) : 235 - 306.
  • 10KENNEDY J, EBERHART R C. Swarm intelligence[ M ]. [ S. 1. ] :Morgan Kaufmann Publishers,2001.

共引文献27

同被引文献76

引证文献8

二级引证文献37

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