Information theory and the finite-time behavior of the simulated annealing algorithm: Experimental results

INFORMS Journal on Computing

Volumn 11, Issue 1, 1999, Pages 35-43

  Fleischer, Mark ^a   Jacobson, Sheldon H ^b  

^a Old Dominion University   (United States)

^b State University   (United States)

Author keywords

Entropy; Information theory; Simulated annealing

Indexed keywords

EID: 0002104441     PISSN: 10919856     EISSN: None     Source Type: Journal    
DOI: 10.1287/ijoc.11.1.35     Document Type: Article

References (23)

1. E. AARTS and J. KORST, 1989. Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing, John Wiley & Sons, New York.
2. R.S. BARR, B. GOLDEN, J.P. KELLY, M.G.C. RESENDE, and W.R. STEWART, JR., 1996. Designing and Reporting on Computational Experiments with Heuristic Methods, Journal of Heuristics 1, 9-32.
3. J.A. BONDY and U.S.R. MURTY, 1976. Graph Theory with Applications, North-Holland, Elsevier Science Publishing Co., Inc., New York.
4. M. FLEISCHER, 1993. Assessing the Performance of the Simulated Annealing Algorithm Using Information Theory, Doctoral dissertation, Case Western Reserve University, Cleveland, OH.
5. M. FLEISCHER and S.H. JACOBSON, 1998. Assessing the Finite-Time Performance of the Simulated Annealing Algorithm Using Information Theory. Technical Report, Old Dominion University, Norfolk, VA.
6. M. FLEISCHER, 1998. Cybernetic Optimization by Simulated Annealing: Solving Continuous Variable Problems. Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization, Ch. 28, Kluwer Academic Publishers, Norwell, MA, 403-418.
7. M. GAREY and D.S. JOHNSON, 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman and Co., New York.
8. C. GOLDIE and R. PINCH, 1991. Communication Theory, Cambridge University Press, New York.
9. L. GOLDSTEIN and M. WATERMAN, 1988. Neighborhood Size in the Simulated Annealing Algorithm, American Journal of Mathematical and Management Sciences 8, 409-423.
10. J.N. HOOKER, 1993. Needed: An Empirical Science of Algorithms, Operations Research 42, 201-212.
11. J.N. HOOKER, 1995. Testing Heuristics: We Have It All Wrong, Journal of Heuristics 1, 33-42.
12. J.N. HOOKER, 1993. Correspondence.
13. D.S. JOHNSON, C.R. ARAGON, L.A. MCGEOCH, and C. SCHEVON, 1989. Optimization by Simulated Annealing: An Experimental Evaluation, Part I (Graph Partitioning), Operations Research 37, 865-892.
14. A.I. KHINCHIN, A.I., 1957. Mathematical Foundations of Information Theory, Dover Publications, Inc., New York.
15. P. L'ECUYER, 1996. Simulation of Algorithms for Performance Analysis, INFORMS Journal on Computing 8, 16-20.
16. U. MANBER, 1989. Introduction to Algorithms: A Creative Approach, Addison-Wesley Publishing Company, New York.
17. C.C. MCGEOCH, 1996. Toward an Experimental Method for Algorithm Simulation, INFORMS Journal on Computing 8, 1-15.
18. C.C. MCGEOCH, 1996. Challenges in Algorithm Simulation, INFORMS Journal on Computing 8, 27-28.
19. D.F. MITRA, D., F. ROMEO, and A. SANGIOVANNI-VINCENTELLI, 1986. Convergence and Finite-Time Behavior of Simulated Annealing, Advances in Applied Probability 18, 747-771.
20. J.B. ORLIN, 1996. On Experimental Methods for Algorithm Simulation, INFORMS Journal on Computing 8, 21-23.
21. R.H.J.M. OTTEN and L.P.P.P. VAN GINNEKEN, 1989. The Annealing Algorithm, Kluwer Academic Publishers, Norwell, MA.
22. J.R. PIERCE, 1980. An Introduction to Information Theory: Symbols, Signals and Noise, 2nd Ed., Dover Publications, Inc., New York.
23. D.R. SHIER, 1996. On Algorithm Analysis, INFORMS Journal on Computing 8, 24-26.