Adaptive Search Algorithms for Discrete Stochastic Optimization: A Smooth Best-Response Approach

Omid Namvar Gharehshiran; Vikram Krishnamurthy; George Yin

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Adaptive Search Algorithms for Discrete Stochastic Optimization: A Smooth Best-Response Approach

机译：离散随机优化的自适应搜索算法：光滑的最佳响应方法

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摘要

This paper considers simulation-based optimization of the performance of a regime-switching stochastic system over a finite set of feasible configurations. Inspired by the stochastic fictitious play learning rules in game theory, we propose an adaptive random search algorithm that uses a smooth best-response sampling strategy and tracks the set of global optima, yet distributes the search so that most of the effort is spent on simulating the system performance at the global optima. The algorithm responds properly to the random unpredictable jumps of the global optimum even when the observations data are temporally correlated as long as a weak law of large numbers holds. Numerical examples show that the proposed scheme yields faster convergence and superior efficiency for finite sample lengths compared with several existing random search and pure exploration methods in the literature.

机译：本文考虑了有限组可行配置下基于模拟的政权切换随机系统性能的优化。受到博弈论中随机虚拟游戏学习规则的启发，我们提出了一种自适应随机搜索算法，该算法使用平滑的最佳响应采样策略并跟踪全局最优集，但分配了搜索范围，因此大部分工作都花在了模拟上全局最佳状态下的系统性能。只要观测数据在时间上相关，只要保持弱的大数定律，该算法就可以对全局最优的随机不可预测的跳跃做出适当的响应。数值算例表明，与文献中现有的几种随机搜索和纯探索方法相比，该方法在有限样本长度下收敛速度更快，效率更高。

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来源
《Automatic Control, IEEE Transactions on》 |2017年第1期|161-176|共16页
作者
Omid Namvar Gharehshiran; Vikram Krishnamurthy; George Yin;
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作者单位

Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada;

Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada;

Department of Mathematics, Wayne State University, Detroit, MI, USA;

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正文语种 eng
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关键词
Optimization; Approximation algorithms; Stochastic processes; Algorithm design and analysis; Linear programming; Adaptation models; Convergence;

机译：优化;近似算法;随机过程;算法设计与分析;线性规划;适应模型;收敛;

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1. Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails [J] . Mansuck Kim, Byung-Jun Yoon BMC Genomics . 2012,第SUPPLEMENTa6期

机译：自适应参考更新（ARU）算法。一种有效优化多种药物鸡尾酒的随机搜索算法
2. Discrete optimization via gradient-based adaptive stochastic search methods [J] . Xi Chen, Enlu Zhou, Jiaqiao Hu AIIE Transactions . 2018,第9期

机译：通过基于梯度的自适应随机搜索方法进行离散优化
3. DISCRETE OPTIMIZATION VIA APPROXIMATE ANNEALING ADAPTIVE SEARCH WITH STOCHASTIC AVERAGING [J] . Jiaqiao Hu, Chen Wang Proceedings of the Workshop on Principles of Advanced and Distributed Simulation . 2011,第CDaROM期

机译：通过随机平均的近似退火自适应搜索进行离散优化
4. Best-response search algorithms for non-stationary discrete stochastic optimization [C] . Gharehshiran Omid Namvar, Krishnamurthy Vikram, Yin George IEEE Annual Conference on Decision and Control . 2014

机译：非平稳离散随机优化的最佳响应搜索算法
5. A local search algorithm approach to analyzing the complexity of discrete optimization problems. [D] . Armstrong, Derek Elswick. 2002

机译：一种用于分析离散优化问题复杂性的局部搜索算法。
6. Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails [O] . Mansuck Kim, Byung-Jun Yoon 2012

机译：自适应参考更新（ARU）算法。一种有效优化多药鸡尾酒的随机搜索算法
7. Adaptive Search Algorithms for Discrete Stochastic Optimization: A Smooth Best-Response Approach [O] . Omid Namvar Gharehshiran, Vikram Krishnamurthy, George Yin 2016

机译：离散随机优化的自适应搜索算法：一种平滑的最佳响应方法

Adaptive Search Algorithms for Discrete Stochastic Optimization: A Smooth Best-Response Approach

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