QPSO算法与PSO算法求解二维热传导反问题

Research on heat conduction inverse problem based on quantum-behaved particle swarm optimization and particle 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。在介绍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。为了提高算法的收敛性和稳定性,在具体应用中对算法进行了改进,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算法的性能比经典PSO算法更加优越,证明QPSO在热传导领域具有很大的实际应用价值。

The research of heat conduction inverse problem starts late in domestic, there are lots of research methods, but ordinary methods are hard to be at the holistic best point. Purpose is to study the application of quantum-behaved particle swarm optimization （QPSO） in the two-dimensional heat conduction on the ground of classical particle swarm optimization （PSO） and how to use the above algorithm based on the objective function to seek the best parameter combination is introduced. In order to enhance the astringency and the stability of the algorithm, and the massive experiments are carried out. The results show in the solution heat conduction inverse problem optimization question, based on QPSO algorithm the performance is better than the classics PSO algorithm, and prove QPSO had determinate practical application value in the heat conduction domain.