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.