Abstract:

With thermo-gram, parameters of tumor inside can be estimated, and an inverse heat conduction model with unknown inner heat source could be obtained from it, and the solving process need a large number solutions of the heat conduction problem, where temperature field in the sub-domain is calculated. For 3D model, it needs a relatively long time. Particle swarm optimization combined with least square methods was applied to solve the inverse problem, in which least square method was used to predict particle's value of fitness function. During the solution process, some of the particles are going to be excluded from the group, by the judgment of new definition of distance. Hence, these particles' positions were rearranged. This method consumes less time than the modified PSO mentioned above, without sacrificing accuracy. Prediction coefficient was analyzed to find how it influences the searching process. So linear decreasing prediction coefficient was applied. Numerical verification shows that above method can reduce the numbers of solution of heat conduction, shorten the solving time, without sacrificing accuracy.

Key words: inverse heat conduction problem, least square method, prediction, algorithm, imaging