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

Omid Namvar Gharehshiran, Vikram Krishnamurthy, George Yin
2017 IEEE Transactions on Automatic Control  
Inspired by the stochastic fictitious play learning rules in game theory, we propose an adaptive simulation-based search algorithm that uses a smooth best-response sampling strategy and tracks the set  ...  This paper considers simulation-based optimization of the performance of a regime-switching stochastic system over a finite set of feasible configurations.  ...  The smooth best-response sampling strategy is then defined as follows.  ... 
doi:10.1109/tac.2016.2539225 fatcat:g4lq5sylfja5xddsomunt5lwci

Reactive Search Optimization: Learning While Optimizing [chapter]

Roberto Battiti, Mauro Brunato
2010 International Series in Operations Research and Management Science  
Reactive Search Optimization has to do with learning for optimizing, with the insertion of a machine learning component into a solution process so that algorithm selection, adaptation, integration, are  ...  The final purpose of Reactive Search Optimization (RSO) is to simplify the life for the final user of optimization.  ...  The work presents an adaptive stochastic search algorithm for the optimization of functions of continuous variables where the only hypothesis is the pointwise computability of the function.  ... 
doi:10.1007/978-1-4419-1665-5_18 fatcat:vimnftpcvrfw7cresqaddwhp64

Stochastic optimization [chapter]

2013 Business Risk Management  
Spall, Introduction to Stochastic Search and Optimization, c 2003 by John Wiley and Sons, Inc.  ...  Stochastic Optimization 195 Program. Selected parts of this article have been reprinted, by permission, from J.C.  ...  This adaptive SPSA (ASP) approach produces a stochastic analogue to the deterministic Newton-Raphson algorithm (e.g., Bazaraa et al., 1993, pp. 308-312) , leading to a recursion that is optimal or near-optimal  ... 
doi:10.1002/9781118749388.ch7 fatcat:3wn5ahnamrhy5j6j6yop6lh3ym

Stochastic Optimization [chapter]

James C. Spall
2011 Handbook of Computational Statistics  
Spall, Introduction to Stochastic Search and Optimization, c 2003 by John Wiley and Sons, Inc.  ...  Stochastic Optimization 195 Program. Selected parts of this article have been reprinted, by permission, from J.C.  ...  This adaptive SPSA (ASP) approach produces a stochastic analogue to the deterministic Newton-Raphson algorithm (e.g., Bazaraa et al., 1993, pp. 308-312) , leading to a recursion that is optimal or near-optimal  ... 
doi:10.1007/978-3-642-21551-3_7 fatcat:chitevoltveghmuft2q4x3nfam

Stochastic Optimization [chapter]

2013 Encyclopedia of Operations Research and Management Science  
Spall, Introduction to Stochastic Search and Optimization, c 2003 by John Wiley and Sons, Inc.  ...  Stochastic Optimization 195 Program. Selected parts of this article have been reprinted, by permission, from J.C.  ...  This adaptive SPSA (ASP) approach produces a stochastic analogue to the deterministic Newton-Raphson algorithm (e.g., Bazaraa et al., 1993, pp. 308-312) , leading to a recursion that is optimal or near-optimal  ... 
doi:10.1007/978-1-4419-1153-7_200813 fatcat:bc46tc3ujzaandex2dji36iupa

Simulation optimization

Yolanda Carson, Anu Maria
1997 Proceedings of the 29th conference on Winter simulation - WSC '97  
Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility.  ...  The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simulation experiment.  ...  For optimizing a kanban sizing problem, utilized a two-phase approach -ES followed by Hooke-Jeeves search method -to obtain "good" solutions with 60% fewer simulation runs than with ES alone. 2.6 Statistical  ... 
doi:10.1145/268437.268460 fatcat:biouqls7gvhhrj3k5tzlhkgi5a

Robust optimization of noisy blackbox problems using the Mesh Adaptive Direct Search algorithm

Charles Audet, Amina Ihaddadene, Sébastien Le Digabel, Christophe Tribes
2018 Optimization Letters  
Blackbox optimization problems are often contaminated with numerical noise, and direct search methods such as the Mesh Adaptive Direct Search (MADS) algorithm may get stuck at solutions artificially created  ...  We propose a way to smooth out the objective function of an unconstrained problem using previously evaluated function evaluations, rather than resampling points.  ...  The approach is named Robust-MADS because it is embedded into the Mesh Adaptive Direct Search (MADS) blackbox optimization algorithm.  ... 
doi:10.1007/s11590-017-1226-6 fatcat:nssgayiea5arjjo3txhl5llefq

Discrete stochastic optimization using linear interpolation

Honggang Wang, Bruce W. Schmeiser
2008 2008 Winter Simulation Conference  
Numerical experiments show that our method finds the optimal solutions for discrete stochastic optimization problems orders of magnitude faster than existing random search algorithms.  ...  We consider discrete stochastic optimization problems where the objective function can only be estimated by a simulation oracle; the oracle is defined only at the discrete points.  ...  Continuous Relaxation To do continuous search in the solution space we create a continuous response surface for the discrete problem IP.  ... 
doi:10.1109/wsc.2008.4736106 dblp:conf/wsc/WangS08 fatcat:hsi2uvhtnjfdffudgogntl7nmy

11 Stochastic Optimization [chapter]

1982 Pure and Applied Mathematics  
Spall, Introduction to Stochastic Search and Optimization, c 2003 by John Wiley and Sons, Inc.  ...  Stochastic Optimization 195 Program. Selected parts of this article have been reprinted, by permission, from J.C.  ...  This adaptive SPSA (ASP) approach produces a stochastic analogue to the deterministic Newton-Raphson algorithm (e.g., Bazaraa et al., 1993, pp. 308-312) , leading to a recursion that is optimal or near-optimal  ... 
doi:10.1016/s0079-8169(08)61221-0 fatcat:j3xrikpjyfaszcm6xrz3vnnubu

Simulation optimization: a review of algorithms and applications

Satyajith Amaran, Nikolaos V. Sahinidis, Bikram Sharda, Scott J. Bury
2014 4OR  
Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation.  ...  To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms  ...  Similar to Tabu search, Scatter Search also utilize adaptive memory in storing best solutions (Glover and Laguna 2000; Martí et al. 2006) . Algorithm 5 provides the scatter search algorithm.  ... 
doi:10.1007/s10288-014-0275-2 fatcat:vhxf65lcpvhibgka3fm7q2avfu

Memory-based Stochastic Optimization

Andrew W. Moore, Jeff G. Schneider
1995 Neural Information Processing Systems  
We compare the new algorithms with a highly tuned higher-order stochastic optimization algorithm on randomly-generated functions and a simulated manufacturing task.  ...  In this paper we introduce new algorithms for optimizing noisy plants in which each experiment is very expensive.  ...  INTRODUCTION In a stochastic optimization problem, noisy samples are taken from a plant.  ... 
dblp:conf/nips/MooreS95 fatcat:5zwaxctdt5gk3nidk374kav4ne

Simulation optimization: a review of algorithms and applications

Satyajith Amaran, Nikolaos V. Sahinidis, Bikram Sharda, Scott J. Bury
2015 Annals of Operations Research  
Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation.  ...  To address specific features of a particular simulation---discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise---various algorithms  ...  Similar to Tabu search, Scatter Search also utilize adaptive memory in storing best solutions [73, 133] . Algorithm 5 provides the scatter search algorithm.  ... 
doi:10.1007/s10479-015-2019-x fatcat:basfhipyijbwdaiajbhyb4v65y

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Ming NIU, Can WAN, Zhao XU
2014 Journal of Modern Power Systems and Clean Energy  
Due to the high complexity with continuous and discrete control variables, modern heuristic optimization algorithms (HOAs) have been widely employed for the solution of OPF.  ...  Optimal power flow (OPF) is one of the key tools for optimal operation and planning of modern power systems.  ...  In the hybrid approach, GA was responsible for solving the discrete optimization with the continuous variables, and the IPM is responsible for solving the continuous optimization with the discrete variables  ... 
doi:10.1007/s40565-014-0089-4 fatcat:xfdyurpzrvgxxiwnp2d4bzgnsy

Derivative-free optimization methods [article]

Jeffrey Larson, Matt Menickelly, Stefan M. Wild
2019 arXiv   pre-print
problems where the output of the black-box oracle is stochastic, and methods for handling different types of constraints.  ...  Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization.  ...  We are especially indebted to Gail Pieper and Glennis Starling for their invaluable editing. This material is based upon work supported  ... 
arXiv:1904.11585v1 fatcat:pvshhbwanvcttigqme3ju32qye

Quantifying uncertainty with ensembles of surrogates for blackbox optimization [article]

Charles Audet, Sébastien Le Digabel, Renaud Saltet
2021 arXiv   pre-print
The method is incorporated in the search step of the mesh adaptive direct search (MADS) algorithm to improve the exploration of the search space.  ...  The results show that the proposed approach solves expensive simulation-based problems at a greater precision and with a lower computational effort than stochastic models.  ...  The proposed approach may be used in any direct search method based on the search-poll paradigm, or in any approach akin to efficient global optimization if adapted.  ... 
arXiv:2107.04360v1 fatcat:iygnxbhvhrcyfjfd6nflx5rnbq
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