Posterior exploration based sequential Monte Carlo for global optimization
Posterior exploration based sequential Monte Carlo for global optimization
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DOI：
10.1007/s10898-017-0543-8
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发表时间：
2015-09
影响因子：
1.8
通讯作者：
B. Liu
中科院分区：
文献类型：
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作者：
B. Liu
提出了一种基于序贯蒙特卡罗(SMC)抽样框架的全局优化算法。在该框架中，将目标函数归一化为概率密度函数(Pdf)，基于概率密度函数设计退火靶pdf序列以渐近收敛于全局最优集。执行序贯重要性抽样程序来模拟结果目标，并从所产生的样本中估计目标函数的最大值。令人困扰的问题在于重要抽样(IS)pdf的设计，它对IS的效率有至关重要的影响。我们提出了一种通过在SMC框架的每次迭代中嵌入后验探索(PE)过程来设计IS PDF的方法。PE过程可以探索由目标PDF支持的解空间的重要区域。PE过程的副产品是设计退火温度计划的自适应机制。我们使用十几个基准函数将所提出的算法与现有的相关方法进行了比较。实验结果表明，该算法具有良好的性能。
We propose a global optimization algorithm based on the sequential Monte Carlo (SMC) sampling framework. In this framework, the objective function is normalized to be a probabilistic density function (pdf), based on which a sequence of annealed target pdfs is designed to asymptotically converge on the set of global optimum. A sequential importance sampling procedure is performed to simulate the resulting targets and the maxima of the objective function are assessed from the yielded samples. The disturbing issue lies in the design of the importance sampling (IS) pdf, which crucially influences the IS efficiency. We propose an approach to design the IS pdf by embedding a posterior exploration (PE) procedure into each iteration of the SMC framework. The PE procedure can explore the important regions of the solution space supported by the target pdf. A byproduct of the PE procedure is an adaptive mechanism to design the annealing temperature schedule. We compare the proposed algorithm with related existing methods using a dozen benchmark functions. The result demonstrates the appealing properties of our algorithm.