基于响应面的概率集群优化方法

Probability collectives using response surface estimation

面对实际应用中的大规模优化问题,基于响应面估计的概率集群优化方法以设计变量的概率分布作为优化对象,而非直接对设计变量值进行优化,可适应连续、离散及混合的设计变量类型。采用响应面构建概率集群评估函数的近似模型,并采用置信区间方法在迭代优化过程中不断更新响应曲面以确保近似精度。实验结果表明算法对解决复杂优化问题有效。

This paper describes the use of Response Surface（RS） with Probability Collectives（PC） to handle large-scale optimization problems.The main characteristic of PC is that it optimizes the probability distribution of the variables rather than their values,thus different types of variables may be integrated into optimization procedure.The RS is used to approximate the utility evaluation of candidate solutions in PC.To improve the approximation accuracy,the Trust Region（TR） method is introduced to iteratively update the RS during optimization.Extensive simulations are conducted to demonstrate the effectiveness of the proposed algorithm.