Research About Heat Conduction Inverse Problem Based On Quantum-Behaved Particles Swarm Optimization

All kinds of heat transference can be discovered in everyday life and engineering practice and all of it have been reduced to three types of heat transference. They are called heat conduction, heat convection and heat radiation. This paper introduces numerical inversion of heat conductivity coefficient based on inverse problem 2D unstable field heat conduction.Research about inverse problem of heat conduction is little late, there are many research methods, but using traditional methods to get global optimization is more difficult. We will research optimization methods of 2D heat conduction using classical Particle Swarm Optimization algorithm (PSO) and Quantum-Behaved Particle swarm optimization algorithm (QPSO), after introducing particle swarm optimization algorithm. And introduce how to optimize heat conductivity coefficient depending on direct problem temperature field and designed objective function. In order to enhance convergence and stability of algorithm, improvement of algorithm is made in experiment. Experiment result indicate that classical Particle Swarm Optimization algorithm and Quantum-Behaved Particle Swarm Optimization algorithm are suitable for solution of inverse problem of heat conduction, that Quantum-Behaved Particle swarm optimization algorithm is more superior to inverse problem of heat conduction than classical Particle Swarm Optimization algorithm. At last, we make a compare between results got from method using QPSO and GA (Genetic Algorithm), because GA have been used in the fields. It shows Quantum-Behaved Particle swarm optimization algorithm has practical value in field of inverse problem of heat conduction.Parallel computation is used to research inverse problem of heat conduction, because it has a big quantity of calculation. MPI is used to constitute parallel condition, and classical Particle Swarm Optimization algorithm and Quantum-Behaved Particle swarm optimization algorithm are used to solute inverse problem of heat conduction based on MPI.