抛物型方程反问题的混合粒子群算法

Inverse problems of parabolic equations of hybrid particle swarm optimization

粒子群优化算法(PSO)是一种新兴的优化技术,它的思想来源于人工生命和演化计算理论。PSO通过粒子追随自己找到的最好解和整个群的最好解来完成优化。粒子群算法简单容易实现,可调参数少,已经得到了广泛研究和应用。提出了一种结合有限元方法求解偏微分方程反问题的混合粒子群算法,在对多个抛物型方程反问题模型测试的数值模拟中都得到了较好的结果,体现了该算法的有效性、通用性和稳健性。

Particle Swarm Optimization（PSO） is a new optimization technique originating from artificial life and evolutionary computation.The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm.PSO can be implemented with ease and few parameters need to be turned.It has been successfully applied in many areas.It can get preferable results in some inverse problems of parabolic equation model＇s numerical simulations.The results show that the approach is effective,general and robust.