TY - JOUR
T1 - A note on the effect of neighborhood structure in simulated annealing
AU - Cheh, Kah Mun
AU - Goldberg, Jeffrey B.
AU - Askin, Ronald
N1 - Funding Information:
* Kah Mun Cheh is a Quality Engineer for Timken, Australia, a subsidiary of The Timken Company of Canton, Ohio. He received a B.Sc. in Industrial Engineering from Iowa State Unive~ity and an M.Sc. in Indust~al Engin~~ng from The University of Arizona. His eurrent interests include quality controf and scrap reduction in metal cutting operations. ?Je&ey B. Goldberg is an Assistant Professor of Systems and Industrial Engineering at The University ol Arizona. He received his Ph.D. in Operations Engineering from The University of Michigan. His research interests include numerical methods for optimi~tion and approximating performance in queueing systems. Some of his articles have appeared, or will appear in the near future, in Stochusric Models, Operarions Resemch, Transpar?aiian Science, European Jownul C$ Operarional Researcl~ and international Journal qf Producrian Research. z Ronald G. Askin is an Associate Professor of Systems and industrial Engineering at the University of Arizona. Tucson. Prior to this appointment. he served on the faculty of The University of Iowa. Dr Askin received a BSc in lnd~t~al Engineering from Lehigh University. and an MSc in Operations Research and Ph.D. in Industrial and Systems Engineering from Georgia institute of Technology. He has published in various professional journals, predominantly in the areas of pr~uction system design and analysis and ensnaring statistics. Dr Askin has received several awards, including the IIE Transactions Development and Applications Award {coauthor), the ASEEjIIE Eugene L. Grant Award (coauthor) and a Nationaf Science Foundation Presidential Young Investigator Award.
PY - 1991
Y1 - 1991
N2 - The simulated annealing method is a neighborhood search algorithm that can be used as a heuristic for many combinatorial problems. Recent research has concentrated on the viability of the approach and the appropriate algorithm parameter settings to use in implementation. In this note we consider a problem specific parameter; the neighborhood structure. We motivate the importance of considering the neighborhood by appealing to results on the convergence rate of simulated annealing and previous empirical results. We test several neighborhood structures on four different problems: the traveling salesman problem, the quadratic assignment problem, the quadratic selection problem and the stochastic quadratic selection problem. Our results suggest that for these problem classes and the particular annealing schedule used, small neighborhoods are better.
AB - The simulated annealing method is a neighborhood search algorithm that can be used as a heuristic for many combinatorial problems. Recent research has concentrated on the viability of the approach and the appropriate algorithm parameter settings to use in implementation. In this note we consider a problem specific parameter; the neighborhood structure. We motivate the importance of considering the neighborhood by appealing to results on the convergence rate of simulated annealing and previous empirical results. We test several neighborhood structures on four different problems: the traveling salesman problem, the quadratic assignment problem, the quadratic selection problem and the stochastic quadratic selection problem. Our results suggest that for these problem classes and the particular annealing schedule used, small neighborhoods are better.
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U2 - 10.1016/0305-0548(91)90059-Z
DO - 10.1016/0305-0548(91)90059-Z
M3 - Article
AN - SCOPUS:0025903204
SN - 0305-0548
VL - 18
SP - 537
EP - 547
JO - Computers and Operations Research
JF - Computers and Operations Research
IS - 6
ER -