应用QPSO算法求解二维热传导反问题

Research about heat conduction inverse problem based on quantum-behaved particles swarm optimization

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。在经典的微粒群优化算法(PSO)的基础上,通过研究基于量子行为的微粒群优化算法(QPSO)提出了应用基于量子行为的微粒群优化算法进行二维热传导参数优化,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。

Research about heat conduction inverse problem is late in domestic,there are lots of the research technique,but ordinary methods are hard to be at the holistic best point.The Quantum-Behaved Particle Swarm Optimization（QPSO） based on classical Particle Swarm Optimization（PSO） is used to study the two-dimenslonal heat conduction inverse problem,and introduces 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,the algorithm is improved,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 proves QPSO having deteminate practical application value in the heat conduction domain.