Affiliation:
1. Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, New York 11794
Abstract
Many systems arising in applications from engineering design, manufacturing, and healthcare require the use of simulation optimization (SO) techniques to improve their performance. In “Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization,” Q. Zhang and J. Hu propose a randomized approach that integrates ideas from actor-critic reinforcement learning within a class of adaptive search algorithms for solving SO problems. The approach fully retains the previous simulation data and incorporates them into an approximation architecture to exploit knowledge of the objective function in searching for improved solutions. The authors provide a finite-time analysis for the method when only a single simulation observation is collected at each iteration. The method works well on a diverse set of benchmark problems and has the potential to yield good performance for complex problems using expensive simulation experiments for performance evaluation.
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Subject
Management Science and Operations Research,Computer Science Applications
Cited by
1 articles.
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