Degree Type

Thesis

Date of Award

2008

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Douglas D. Gemmill

Second Advisor

John K. Jackman

Third Advisor

Karin Dorman

Abstract

In this thesis we examine the performance of simulated annealing (SA) on various response surfaces. The main goals of the study are to evaluate the effectiveness of SA for stochastic optimization, develop modifications to SA in an attempt to improve its performance, and to evaluate whether artificially adding noise to a deterministic response surface might improve the performance of SA. SA is applied to several different response surfaces with different levels of complexity. We first experiment with two basic approaches of computing the performance measure for stochastic surfaces, constant sample size and variable sample size. We found that the constant sample size performed best. At the same time we also show that artificially adding noise may improve the performance of SA on more complex deterministic response surfaces. We develop a hybrid version of SA in which the genetic algorithm is embedded within SA. The effectiveness of the hybrid approach is not conclusive and needs further investigation. Finally, we conclude with a brief discussion on the strengths and weaknesses of the proposed method and an outline of future directions.

DOI

https://doi.org/10.31274/rtd-180813-16612

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Xiaoqing Gracie Gu

Language

en

Proquest ID

AAI1454679

OCLC Number

263683591

ISBN

9780549686972

File Format

application/pdf

File Size

64 pages

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