Continuous optimization via simulation using Golden Region search

Date
2009-01-01
Authors
Kabirian, Alireza
Journal Title
Journal ISSN
Volume Title
Publisher
Source URI
Altmetrics
Authors
Research Projects
Organizational Units
Journal Issue
Series
Abstract

Simulation Optimization (SO) is the use of mathematical optimization techniques in which the objective function (and/or constraints) could only be numerically evaluated through simulation. Many of the proposed SO methods in the literature are rooted in or originally developed for deterministic optimization problems with available objective function. We argue that since evaluating the objective function in SO requires a simulation run which is more computationally costly than evaluating an available closed form function, SO methods should be more conservative and careful in proposing new candidate solutions for objective function evaluation. Based on this principle, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. The experiments show the GR method is efficient compared to three well-established approaches in the literature. We also prove the convergence in probability to global optimum for a large class of random search methods in general and GR in particular.

Description
Keywords
Continuous Optimization, Discrete-Event Simulation, Golden Region Search, Probabilistic Search, Ranking and Selection, Simulation Optimization
Citation