Chapter 21 Metaheuristics

Handbooks in Operations Research and Management Science

Volumn 13, Issue C, 2006, Pages 633-654

  Ólafsson, Sigurdur ^a  

^a Iowa State University of Science and Technology   (United States)

Author keywords

convergence analysis; genetic algorithms; metaheuristics; nested partitions method; Simulation optimization; tabu search

Indexed keywords

EID: 77950463112     PISSN: 09270507     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0927-0507(06)13021-2     Document Type: Review

References (53)

1. April J., Glover F., Kelly J.P., and Laguna M. Practical introduction to simulation optimization. Proceedings of the 2003 Winter Simulation Conference (2003), IEEE Press, Piscataway, NJ 71-78
2. Aytug H., Bhattacharrya S., and Koehler G.J. A Markov chain analysis of genetic algorithms with power of 2 cardinality alphabets. European Journal of Operational Research 96 (1996) 195-201
3. Azadivar F., and Wang J. Facility layout optimization using simulations and genetic algorithms. International Journal of Production Research 38 17 (2000) 4369-4383
4. Back T. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms (1996), Oxford University Press, New York
5. Boesel J., Nelson B.L., and Ishii N. A framework for simulation optimization software. IIE Transactions 35 3 (2003) 221-229
6. Boesel J., Nelson B.L., and Kim S. Using ranking and selection to "clean-up" after simulation optimization. Operation Research 51 5 (2003) 814-825
7. Dai L. Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems. Journal of Optimization Theory & Applications 91 2 (1996) 363-388
8. Dasgupta D., and Mcgregor D.R. Nonstationary function optimization using the structured genetic algorithm. Parallel Problem Solving from Nature 2 (1992) 145-154
9. Eglese R.W. Simulated annealing: A tool for operation research. European Journal of Operational Research 46 (1990) 271-281
10. Faccenda J.F., and Tenga R.F. A combined simulation/optimization approach to process plant design. Proceedings of the 1992 Winter Simulation Conference (1992), IEEE Press, Piscataway, NJ 1256-1261
11. Fleischer M.A. Simulated annealing: Past present and future. Proceedings of the 1995 Winter Simulation Conference (1995), IEEE Press, Piscataway, NJ 155-161
12. Fu M.C. Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing 14 3 (2002) 192-215
13. Glover F. Tabu Search - Part I. ORSA Journal on Computing 1 (1989) 190-206
14. Glover F. Tabu Search - Part II. ORSA Journal on Computing 2 (1990) 4-32
15. Glover F., and Hanafi S. Tabu search and finite convergence. Discrete Applied Mathematics 119 (2002) 3-36
16. Glover F., and Kochenberger G.A. Handbook of Metaheuristics (2003), Kluwer Academic Publishers, Boston, MA
17. Glover F., and Laguna M. Tabu Search (1997), Kluwer Academic Publishers, Boston, MA
18. Glover F., Laguna M., and Marti R. Scatter search. In: Ghosh A., and Tsutsui S. (Eds). Theory and Applications of Evolutionary Computation: Recent Trends (2003), Springer-Verlag
19. Goldberg D.E. Genetic Algorithm in Search, Optimization and Machine Learning (1989), Addison-Wesley, Reading, MA
20. Greenhalgh D., and Marshall S. Convergence criteria for genetic algorithm. SIAM Journal on Computing 30 (2000) 269-282
21. Grefenstette J.J. Genetic Algorithm for Changing Environments. Parallel Problem Solving from Nature 2 (1992), Elsevier, Amsterdam, The Netherlands
22. Haddock J., and Mittenhall J. Simulation optimization using simulated annealing. Computer and Industrial Engineering 22 (1992) 387-395
23. Hall J.D., and Bowden R.O. Simulation optimization by direct search: A comparative study. Proceedings of the 6th International Industrial Engineering Research Conference (1997), IEE, Norcross, GA 298-303
24. Hanafi S. On the convergence of tabu search. Journal of Heuristics 7 (2000) 47-58
25. Hansen P., and Mladenovic N. An introduction to variable neighborhood search. In: Voss S., et al. (Ed). Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization (1999), Kluwer Academic Publishers, Boston, MA 433-458
26. Ho Y.C., Sreenivas R., and Vakili P. Ordinal optimization of discrete event dynamic systems. Journal of DEDS 2 2 (1992) 61-88
27. Ho Y.C., Cassandras C.G., Chen C.-H., and Dai L. Ordinal optimization and simulation. Journal of the Operational Research Society 51 (2000) 490-500
28. Holland J.H. Adaptation in Natural and Artificial Systems (1975), University of Michigan Press, Ann Arbor, MI
29. Ishibuchi H., and Murata T. Multi-objective genetic local search algorithm. Proceedings of IEEE International Conference on Evolutionary Computation (1996), IEEE Press, Piscataway, NJ 119-124
30. Jacobson S.H., Sullivan K.A., and Johnson A.W. Discrete manufacturing process design optimization using computer simulation and generalized hill-climbing algorithm. Engineering Optimization 31 (1998) 247-260
31. Johnson A.W., and Jacobson S.H. On the convergence of generalized hill climbing algorithms. Discrete Applied Mathematics 119 (2002) 37-57
32. Kelly J.P. Simulation optimization is evolving. INFORMS Journal on Computing 14 3 (2002) 223-225
33. Kim J., and Ólafsson S. Two-stage NP method with inheritance. Proceedings of the 2002 Winter Simulation Conference (2002), IEEE Press, Piscataway, NJ 279-284
34. Kirkpatrick S., Gelatt Jr. C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 (1983) 671-680
35. Liepins G.E. On global convergence of genetic algorithms. SPIE Neutral and Stochastic Methods in Image and Signal Processing 1766 (1992) 61-65
36. Liepins G.E., and Hilliard M.R. Genetic algorithm: Foundations and applications. Annals of Operations Research 21 (1989) 31-58
37. Muhlenbein H. Genetic algorithms. In: Aarts E., and Lenstra J.K. (Eds). Local Search in Combinatorial Optimization (1997), Wiley, New York 137-172
38. Ólafsson S. Two-stage nested partitions method for stochastic optimization. Methodology and Computing in Applied Probability 6 (2004) 5-27
39. Ólafsson S., and Kim J. Simulation optimization. Proceedings of the 2002 Winter Simulation Conference (2002), IEEE Press, Piscataway, NJ 79-84
40. Pichitlamken J., and Nelson B.L. A combined procedure for optimization via simulation. ACM Transactions on Modeling and Computer Simulation 13 2 (2003) 108-133
41. Resende M.G.C., and Ribeiro C.C. Greedy randomized adaptive search procedures. In: Glover A., and Kochenberger A. (Eds). Handbook of Metaheuristics (2003), Kluwer Academic Publishers, Boston, MA
42. Reynolds D., and Gomatam J. Stochastic modeling of genetic algorithms. Artificial Intelligence 82 (1996) 303-330
43. Shi L., and Chen C.H. A new algorithm for stochastic discrete resource allocation optimization. Journal of Discrete Event Dynamic Systems: Theory and Applications 10 (2000) 271-294
44. Shi L., and Men S. Optimal buffer allocation in production lines. IIE Transactions 35 (2003) 1-10
45. Shi L., and Ólafsson S. Nested partitions method for global optimization. Operations Research 48 (2000) 390-407
46. Shi L., and Ólafsson S. Nested partitioned method for stochastic optimization. Methodology and Computing in Applied Probability 2 (2000) 271-291
47. Shi L., Ólafsson S., and Chen Q. A new hybrid optimization method. Computers and Industrial Engineering 36 (1999) 409-426
48. Sullivan K.A., and Jacobson S.H. A convergence analysis of generalized hill climbing algorithms. IEEE Transaction on Automatic Control 46 8 (2001) 1288-1293
49. Tian P., Ma J., and Fan Z. A stochastic tabu search strategy and its global convergence. IEEE International Conference on Systems, Man and Cybernetics (1997), IEEE Press, Piscataway, NJ 410-414
50. Tompkins G., and Azadivar F. Genetic algorithms in optimizing simulated systems. Proceedings of the 1995 Winter Simulation Conference (1995), IEEE Press, Piscataway, NJ 757-762
51. Vavak F., and Fogarty T.C. Comparison of steady state and generational genetic algorithms for use in nonstationary environments. Proceedings of IEEE International Conference on Evolutionary Computation (1996), IEEE Press, Piscataway, NJ 192-195
52. Watson J., Beck J.C., Howe A.E., and Whitley L.D. Problem difficulty for tabu search in job-shop scheduling. Artificial Intelligence 143 2 (2003) 189-217
53. Yoshitomi Y., and Yamaguchi R. A genetic algorithm and the Monte Carlo method for stochastic job-shop scheduling. International Transaction of Operational Research 10 (2003) 577-596