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Parallelizing simulated annealing algorithms based on high-performance computer

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We implemented five conversions of simulated annealing (SA) algorithm from sequential-to-parallel forms on high-performance computers and applied them to a set of standard function optimization problems in order to test their performances. According to the experimental results, we eventually found that the traditional approach to parallelizing simulated annealing, namely, parallelizing moves in sequential SA, difficultly handled very difficult problem instances. Divide-and-conquer decomposition strategy used in a search space sometimes might find the global optimum function value, but it frequently resulted in great time cost if the random search space was considerably expanded. The most effective way we found in identifying the global optimum solution is to introduce genetic algorithm (GA) and build a highly hybrid GA+SA algorithm. In this approach, GA has been applied to each cooling temperature stage. Additionally, the performance analyses of the best algorithm among the five implemented algorithms have been done on the IBM Beowulf PCs Cluster and some comparisons have been made with some recent global optimization algorithms in terms of the number of functional evaluations needed to obtain a global minimum, success rate and solution quality.

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  1. Wah B.W. and Chang Y.-J. (1997). Trace-based methods for solving nonlinear global optimization problems. J. Global Optimiz. 10(2): 107–141

    Article  Google Scholar 

  2. Szu H. and Hartley R. (1987). Fast simulated annealing. Phys. Lett. A. 122: 157–162

    Article  Google Scholar 

  3. Rosen, B.E.: GA’s and very fast simulated reannealing. Genetic Algorithms Digest 5(36), (1991) An Electronic Journal

  4. Ingber, L., Rosen, B.: Very fast simulated reannealing (VFSR),, AT&T Bell labs, Murray Hill, NJ (1992)

  5. Ingber L. (1994). Simulated annealing: practice versus theory. J. Math. Comput. Model 18(11): 29–57

    Article  Google Scholar 

  6. Siarry P., Berthiau G., Durbin F. and Haussy J. (1997). Enhanced simulated annealing for globally minimizing functions of many-continuous variables. ACM Trans. Math. Software 23(2): 209–228

    Article  Google Scholar 

  7. Ellen A.F., Roger E.W. and Mark D.C (1991). Parallel simulated annealing using speculative computation. IEEE Trans. Parallel Distribut Syst. 2(4): 483–494

    Article  Google Scholar 

  8. Hamma, B.S., Viitanen, S., Torn, A.: Parallel continuous simulated annealing for global optimization. Presented at the NATO Advance Study Institute – Algorithms for Continuous Optimization: The State of the Art, II Ciocco-castelvecchio Pascoli, Italy (1993)

  9. Yong L., lishan K. and Evans D.J. (1995). The annealing evolution algorithm as function optimizer. Parallel Computing 21(3): 389–400

    Article  Google Scholar 

  10. Chen H., Flann N.S. and Watson D.W. (1998). Parallel genetic simulated annealing: a massively parallel SIMD algorithm. IEEE Trans. Parallel Distribut Syst. 9: 126–136

    Article  Google Scholar 

  11. Esin O. and Linet O. (2001). Parallel simulated annealing algorithms in global optimization. J. Global Optimiz. 19: 27–50

    Article  Google Scholar 

  12. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H. and Teller E. (1953). Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6): 1087–1092

    Article  Google Scholar 

  13. Kirkpatrick S., Vecchi M.P. and Gelatt C.D. (1983). Optimization by simulated annealing. Science 220(4598): 671–680

    Article  Google Scholar 

  14. Kimura, K., Taki, K.: Time-homogeneous parallel annealing algorithm. Report TR-673, Institute for New Generation Computer Technology, Tokyo, Japan (1991)

  15. Mahfoud, S.W., Goldberg, D.E.: Parallel recombinative simulated annealing :a genetic algorithm. IlliGAL Report No. 92002, University of Illinois, Urbana, IL (1992)

  16. Ter Laak A., Hertzberger L.O. and Sloot P.M.A. (1992). Nonconvex continuous optimization experiments on a transputer system. In: Allen, A. (eds) Transputer Systems-Ongoing Research, pp 251–265. IOS Press, Amsterdam

    Google Scholar 

  17. and

  18. Korf R.E. (1993). Linear-space best-first search. Artif. Intell. 62: 41–78

    Article  Google Scholar 

  19. Pardalos P.M. and Rosen J.B. (1987). Constrained Global Optimization: Algorithms and Applications Vol. 268 of Lecture Notes in Computer Science. Springer-Verlag, Berlin

    Google Scholar 

  20. Parker R.G. and Rardin R.L. (1988). Discrete Optimization. Academic Press Inc., San Diego, CA

    Google Scholar 

  21. Kennedy J. and Eberhart R.C. (2001). Swarm intelligence. Morgan Kaufmann Publishers, Los Altos

    Google Scholar 

  22. Torn A. and Zilinskas A. (1989). Global optimization. Springer-Verlag, Berlin

    Google Scholar 

  23. Cetin B.C., Barhen J. and Burdick W. (1993). Terminal Pepeller unconstrained subenergy tunneling (TRUST) for fast global optimization. J. Optimiz. Theory Appl. 77(1): 97–127

    Article  Google Scholar 

  24. Theodore B.T. and Suat K. (2002). A novel metaheuristics approach for continuous global optimization. J. Global Optimiz. 23: 171–190

    Article  Google Scholar 

  25. Corana A., Marchesi M., Martini C. and Ridella S. (1987). Minimizing multimodal functions of continuous variables with the simulated annealing algorithm. ACM Trans. Math. Software 13(3): 262–279

    Article  Google Scholar 

  26. Cem, B.: A hybrid parallel simulated annealing algorithm to optimize store performance. Proceedings of Workshop on Evolutionary Computing for Optimisation in Industry at the Genetic and Evolutionary Computation Conference (GECCO-2002), 9 July 2002, New York (2002)

  27. Hussain M.F. and Al-sultan K.S. (1997). A hybrid genetic algorithm for nonconvex function minimization. J. Global Optimiz. 11: 313–324

    Article  Google Scholar 

  28. Alan H.K. and Horace P.F. (1990). Measuring parallel processor performance. Commun. ACM. 33(5): 539–543

    Article  Google Scholar 

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Correspondence to Ding-Jun Chen.

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Chen, DJ., Lee, CY., Park, CH. et al. Parallelizing simulated annealing algorithms based on high-performance computer. J Glob Optim 39, 261–289 (2007).

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