Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (4): 58-60.

• 学术探讨 • Previous Articles     Next Articles

Heat conduction inverse problem using Quantum-Behaved Partical Swarm Optimization

Hong Zhang   

  • Received:2006-05-19 Revised:1900-01-01 Online:2007-02-01 Published:2007-02-01
  • Contact: Hong Zhang

应用QPSO 算法求解二维热传导反问题

张宏 须文波 孙俊   

  1. 江苏无锡江南大学蠡湖校区31#306 江南大学通信与控制工程学院 江南大学信息工程学院
  • 通讯作者: 张宏

Abstract: Research about heat conduction inverse problem be late in domestic, there are lot of the research technique, but ordinary methods are hard to be at the holistic best point. our purpose is to study the application of Quantum-Behaved Partical Swarm Optimization(QPSO) in the two-dimensional heat conduction on the ground of classical Particle Swarm Optimizatio(PSO).And introduce how to use the above algorithm based on the objective function to seek the best parameter combination. In order to enhance the algorithm the astringency and the stability we make the improvement to the algorithm, and has carried on the massive experiments. The result shows in the solution heat conduction inverse problem optimization question, based on QPSO algorithm works well on heat conduction inverse problem , and prove QPSO had deteminate practical application value in the heat conduction domain.

摘要: 热传导反问题在国内研究起步较晚,研究方法有很多,但通常方法很难较好地接近全局最优,本文的目的是在经典的微粒群优化算法(PSO)的基础上,研究基于量子行为的微粒群优化算法(QPSO)的二维热传导参数优化方法,具体介绍依据目标函数如何利用上述的算法去寻找最优参数组合。在具体应用中为了提高算法的收敛性和稳定性对算法进行了改进,并进行了大量实验,结果显示在解决热传导反问题优化问题中,基于QPSO算法的性能优越,证明QPSO在热传导领域具有很大的实际应用价值。