Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (9): 49-50.DOI: 10.3778/j.issn.1002-8331.2010.09.015

• 研究、探讨 • Previous Articles     Next Articles

Quantum-behaved particle swarm optimization for mixed-integer nonlinear programming

ZHANG Lan1,2,XING Zhi-dong1   

  1. 1.Mathematics Department,Northwest University,Xi’an 710127,China
    2.Basic Department,Xi’an Aeronautical Polytechnic Institute,Xi’an 710089,China
  • Received:2009-05-05 Revised:2009-06-30 Online:2010-03-21 Published:2010-03-21
  • Contact: ZHANG Lan


张 兰1,2,邢志栋1   

  1. 1.西北大学 数学系,西安 710127
    2.西安航空职业技术学院 基础部,西安 710089
  • 通讯作者: 张 兰

Abstract: Quantum-behaved Particle Swarm Optimization(QPSO) developed on the basis of classical particle swarm optimization is a method with better convergence for mixed-integer nonlinear programming.Then the experimental results indicate that QPSO handles mixed-integer nonlinear programming problems much efficiently.It is a new way for solving mixed-integer nonlinear programming problem.

Key words: Mixed-integer NonLinear Programming(MNLP), Quantum-behaved Particle Swarm Optimization(QPSO), Particle Swarm Optimization(PSO)

摘要: 在经典微粒群算法的基础上提出一种有较高收敛性能的智能算法:量子粒子群(QPSO)算法。并用于求解混合整数非线性规划问题。实验室证明QPSO算法收敛性能好、速度快,为求解混合整数非线性规划开辟了新途径。

关键词: 混合整数非线性规划(MNLP), 量子粒子群(QPSO), 粒子群(PSO)

CLC Number: