A two-time-scale adaptive search algorithm for global optimization
A two-time-scale adaptive search algorithm for global optimization
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DOI：
10.1109/wsc.2017.8247940
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Qi Zhang-;Jiaqiao Hu
中科院分区：
文献类型：
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作者：
Qi Zhang-;Jiaqiao Hu
我们研究一种随机搜索算法，用于在黑盒方案中解决确定性优化问题。该算法具有基于模型的性质，并通过从可行区域的分布模型中取样来发现改进的解决方案，该区域逐渐将其概率质量集中在高质量解决方案周围。与类基于人群的许多现有算法相反，我们的方法将随机搜索与两次尺度的随机近似想法结合在一起，以解决这些算法固有的一定比率偏差，并且仅在迭代中使用单个候选解决方案。我们证明了算法的全球融合并进行数值实验以说明其性能。
We study a random search algorithm for solving deterministic optimization problems in a black-box scenario. The algorithm has a model-based nature and finds improved solutions by sampling from a distribution model over the feasible region that gradually concentrates its probability mass around high quality solutions. In contrast to many existing algorithms in the class, which are population-based, our approach combines random search with a two-time-scale stochastic approximation idea to address a certain ratio bias inherent in these algorithms and uses only a single candidate solution per iteration. We prove global convergence of the algorithm and carry out numerical experiments to illustrate its performance.