期刊文献+

量子粒子群优化算法在抗震工程系统中的应用

Application of QPSO in the anti-seismic engineering system optimal design with intermediate state
下载PDF
导出
摘要 介绍了标准粒子群算法的基本思想,为了使粒子能够满足全局收敛,提出了量子粒子群算法,将量子粒子群算法应用到抗震工程系统的投资最优分配中,并建立了相应的优化模型,最后通过一个算例验证了该方法的效率和有效性。 This paper introduces the standard PSO basic idea,to enable particles to meet global convergence,a Quantum-behaved Particle Swarm Optimization was introduced.Particle swarm optimization is applied to the cost optimal allocation,and built the optimization model.The paper presents the particle swarm optimization in brief and detailed steps of building model.A numerical example is given to demonstrate efficiency and feasibility.
作者 李献超
出处 《山西建筑》 2008年第16期68-70,共3页 Shanxi Architecture
关键词 量子粒子群算法 抗震工程系统 最优设防烈度 Quantum-behaved Particle Swarm Optimization(QPSO), anti-seismic engineering system,optimal design intensity
  • 相关文献

参考文献4

  • 1Kennedy J, Eberhart R C. Panicle swarm optimization[A]. Proceeding of the 1995 IEEE international conference on neural network [ C]. Perth: [ s . n. ], 1995 : 1942-1948.
  • 2Shi Y and Eberhat RC, A Modified Swarm Optimizer[J]. in Proc. IEEE int Conf of Evolutionary Computation, 1998: 1945-1950.
  • 3杨淑媛,焦李成,刘芳.量子进化算法[J].工程数学学报,2006,23(2):235-246. 被引量:34
  • 4王大均,高兴宝,李华平.基于粒子群算法的钢结构截面优化设计[J].山西建筑,2007,33(18):63-64. 被引量:1

二级参考文献18

  • 1杨淑媛,刘芳,焦李成.量子进化策略[J].电子学报,2001,29(z1):1873-1877. 被引量:32
  • 2董军.H形压弯构件截面设计快速优化方法[J].建筑结构,1995,25(5):18-22. 被引量:7
  • 3Holland J H. Genetic algorithms and classifier systems: foundations and their applications[C]//Proceedings of the Second International Conference on Genetic Algorithms. Hillsdale, N J: Lawrence Erlbaum Associates,1987:82-89.
  • 4Fogel L J, Owens A J, Walsh M J. Artificial Intelligence Through Simulated Evolution[M]. Chichester:John Wiley, 1966.
  • 5Klockgether J, Schwefel H P. Two-phase nozzle and hollow core jet experiments[C]// Elliott D. (eds.)Proc 11th Symp Engineering Aspects of Magneto hydrodynamics. Pasadena CA: California Institute of Technology, March 24-26, 1970:141-148.
  • 6Hey T. Quantum Computing: An Introduction[J]. Computing & Control Engineering Journal,1999,10(3):105-112.
  • 7Alen Varsek. Tanja Urbancic, Bodgan Filipic, Genetic algorithms in controller design and tuning[J]. IEEE Trans S M C, 1993,23(5):1330-1339.
  • 8Miller G, Todd P, Hedge S. Designing neural networks using genetic algorithm[C]//Proceedings of the Third International Conference on Genetic Algorithms and Their applications, D J Schaffer, Ed. San Mateo, CA:Morgan Kaufmann, 1989:360-369.
  • 9Goldberg D E. Genetic algorithm in search, optimization, and machine learning[M]. MA: Addison-Wesley, Reading, MA, 1989.
  • 10Fogel D B. Asymptotic convergency properties of genetic algorithms and evolutionary programming: analysis and experiments[J]. Cybernetic and Systems: An Int J, 1994,25(3):389-407.

共引文献33

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