南科大信管系王松昊助理教授的研究成果被INFORMS Journal on Computing期刊接收

2021-02-24

近日，南方科技大学信息系统与管理工程系王松昊助理教授与其海外合作者的文章Combined Global and Local Search for Optimization with Gaussian Process Models被INFORMS Journal on Computing期刊接收并即将发表。该文由王松昊与新加坡国立大学Meng Qun Szu Hui Ng合作撰写。

摘要：Gaussian process (GP) model based optimization is widely applied in simulation and machine learning. In general it first estimates a GP model based on a few observations from the true response and then employs this model to guide the search aiming to quickly locate the global optimum. Despite its successful applications it has several limitations that may hinder its broader usage. First building an accurate GP model can be difficult and computationally expensive especially when the response function is multi-modal or varies significantly over the design space. Second even with an appropriate model the search process can be trapped in suboptimal regions before moving to the global optimum due to the excessive effort spent around the current best solution. In this work we adopt the Additive Global and Local GP (AGLGP) model in the optimization framework. The model is rooted in the inducing-points-based GP sparse approximations and is combined with independent local models in different regions. With these properties the AGLGP model is suitable for multi-modal responses with relatively large data sizes. Based on this AGLGP model we propose a Combined Global and Local search for Optimization (CGLO) algorithm. It first divides the whole design space into disjoint local regions and identifies a promising region with the global model. Next a local model in the selected region is fit to guide detailed search within this region. The algorithm then switches back to the global step when a good local solution is found. The global and local natures of CGLO enable it to enjoy the benefits of both global and local search to efficiently locate the global optimum.