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中文摘要：
提出一种新的自适应粒子群优化算法，以解决梯度法为基础的算法在进行多参数拟合时因各参数之间相关性较高而带来的拟合上的问题．该粒子群优化算法采用自适应变异和动态自适应调整搜索范围、惯性权重相结合的改进策略，数值模拟了将该算法应用于测量薄膜热物性时的多参数拟合，结果表明该算法是可行和有效的．
英文摘要：
In order to make up the deficit of fitting strong correlation parameters with gradient-based methods, a new adaptive particle swarm algorithm was proposed. A modified strategy was developed by combining a new adaptive mutation and an adaptive adjustment inertia weight, searching regions in the algorithm. The new algo- rithm was used to simulate numerically the multiparameter fitting in the process of characterizing thermal properties of thin films. It was showed that the new algorithm was feasible and efficient
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