Abstract
In this paper, the Evolutionary Simulated Annealing (ESA) algorithm, its distributed implementation (dESA) and its application to two combinatorial problems are presented. ESA consists of a population, a simulated annealing operator, instead of the more usual reproduction operators used in evolutionary algorithms, and a selection operator. The implementation is based on a multi island (agent) system running on the Distributed Resource Machine (DRM), which is a novel, scalable, distributed virtual machine based on Java technology. As WAN/LAN systems are the most common multi-machine systems, dESA implementation is based on them rather than any other parallel machine. The problems tackled are well-known combinatorial optimisation problems, namely, the classical job-shop scheduling problem and the uncapacitated facility location problem. They are difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit (CPU) time spent. Both applications show that dESA solves problems finding either the optimum or a very near optimum solution within a reasonable time outperforming the recent reported approaches for each one allowing the faster solution of existing problems and the solution of larger problems.
Similar content being viewed by others
References
Alba, E. and M.Tomassini. (2002). “Parallelism and Evolutionary Algorithms.” IEEE Transactions on Evolutionary Computation 6(5), 443–462.
Alves, M.L. and M.T. Almeida. (1992). “Simulated Annealing Algorithm for the Simple Plant Location Problem: A Computational Study.” Rev. Invest. 12.
Aydin, M.E. and T.C. Fogarty. (2002). “A Modular Simulated Annealing Algorithm for Multi-Agent Systems: A Job Shop Scheduling Application.” In Proc. of ICRM 2002 (2nd International Conference of Responsive Manufacturing). 26-18 June, Gaziantep, Turkey.
Beasley, J.E. (1993). “Lagrangean Heuristics for Location Problems.” European Journal of Operational Research 65, 383–399.
Beasley, J.E. (1996). “Obtaining Test Problems via Internet.” Journal of Global Optimisation 8, 429–433, http://mscmga.ms.ic.ac.uk/info.html.
Beasley, J.E. and P.C. Chu. (1996). “A Genetic Algorithm For the Set Covering Problem.” European Journal of Operational Research 94, 392–404.
Bevilacqua, A. (2002). “A Methodological Approach to Parallel Simulated Annealing on an SMP System.” Journal of Parallel and Distributed Computing 62(10), 1548–1570.
Bhandarkar, S.M., S. Machaka, S. Chirravuri, and J. Arlond. “Parallel Computing for Chromosome Reconstruction via Ordering of DNA Sequences.” Parallel Computing 24(12/13), 1177-1204.
Boissin, N. and J.L. Lutton. (1993). “A Parallel Simulated Annealing Algorithm.” Parallel Computing 19(8), 859–872.
Bongiovanni, G., P. Crescenzi, and C. Guerra. (1995). “Parallel Simulated Annealing for Shape Detection.” Computer Vision and Image Understanding 61(1), 60–19.
Chu, K.W., Y. Deng, and J. Reinitz. (1999). “Parallel Simulated Annealing by Mixing of States.” Journal of Computational Physics 148(2), 646–662.
Conn, A.R. and G. Cornuejols. (1990). “A Projection Method for the Uncapacitated Facility Location Problem.” Math. Programming 46, 273–298.
Erlenkotter, D. (1978). “A Dual-Based Procedure for Uncapacitated Facility Location.” Operations Research 26, 992–1009.
Ferreira, A.G. and J. Zerovnik. (1993). “Bounding the Probability of Success of Stochastic Methods for Global Optimisation.” Computers & Mathematics with Applications 25(10/11), 1–8.
Greening, D. R. (1990). “Parallel Simulated Annealing Techniques.” Physica D: Nonlinear Phenomena 42(1-3), 293–306.
Guignard, M. (1988). “A Lagrangean Dual Ascent Algorithm for Simple Plant Location Problems.” European Journal of Operational Research 35, 193–200.
Holmberg, K. (1995). “Experiments with Primal-Dual Decomposition and Subgradient Methods for the Uncapacitated Facility Location Problem.” Research Report LiTH-MAT/OPT-WP-1995-08, Optimization. Department of Mathematics, Linkoping Institute of Technology, Sweden.
Holmberg, K. and K. Jörnsten. (1996). “Dual Search Procedures for The Exact Formulation of The Simple Plant Location Problem with Spatial Interaction.” Location Science 4, 83–100.
Huang, H.C., J.S. Pan, Z.M. Lu, S.H. Sun, and H.M. Hang. (2001). “Vector Quantization Based on Genetic Simulated Annealing.” Signal Processing 81, 1513–1523.
Jaramillo, J.H., J. Bhadury, and R. Batta. (2002). “On the Use of Genetic Algorithms to Solve Location Problems.” Computers & Operations Research 29, 761–779.
Jeong, I.K and J.J. Lee. (1996). “Adaptive Simulated Annealing Genetic Algorithm for System Identification.” Engineering Applications of Artificial Intelligence 9(5), 523–532.
Jelasity, M., M. Preu?, and B. Peachter. (2002). “A Scalable and Robust Framework for Distributed Applications.” CEC'02: The 2002 World Congress on Computational Intelligence, Honolulu, HI, USA.
Kim, Y., Y. Jang, and M. Kim. (1991). “Stepwise-Overlapped Parallel Simulated Annealing and its Application to Floorplan Designs.” Computer-Aided Design 23(2), 133–144.
Koerkel, M. (1989). “On the Exact Solution of Large-Scale Simple Plant Location Problems.” European Journal of Operational Research 39, 157–173.
Kirkpatrick, S., C.D. Jr. Gelatt, and M.P. Vecchi. (1938). “Optimisation by Simulated Annealing.” Science 220(4598), 671–679.
Kolonko, M. (1999). “Some New Results on Simulated Annealing Applied to Job Shop Scheduling Problem.” European Journal of Operational Research 113, 123–136.
Kratica, J., D. Tošiæ, V. Filipoviæ, and I. Ljubiæ. (2001). “Solving the Simple Plant Location Problem by Genetic Algorithms.” RAIRO-Operations Research 35(1), 127–142.
Paechter, B., T. Back, M. Schoenauer, M. Sebag, A.E. Eiben, J.J. Merelo, and T.C. Fogarty. (2000). “A Distributed Resource Evolutionary Algorithm Machine (DREAM).” In Proc. of the Congress of Evolutionary Computation 2000 (CEC2000). IEEE, 2000, IEEE Press, pp. 951-958.
Satake, T., K. Morikawa, K. Takahashi, and N. Nakamura. (1999). “Simulated Annealing Approach for Minimising the Makespan of the General Job-Shop.” International Journal of Production Economics 60/61, 515–522.
Schmeck, H., J. Branke, and U. Kohlmorgen. (2001). “Parallel Implementations of Evolutionary Algorithms.” In A. Zomaya, F. Ercal, and S. Olariu (eds.), Solutions to Parallel and Distributed Computing Problems. John Wiley and Sons Inc.
Simao, H.P. and J.M. Thizy. (1989). “A Dual Simplex Algorithm for the Canonical Representation of the Uncapacitated Facility Location Problem.” Operations Research Letters 8, 279–286.
Steinhofel, K., A. Albrecht, and C.K. Wong. (1999). “Two Simulated Annealing-Based Heuristics for the Job Shop Scheduling Problem.” European Journal of Operational Research 118, 524–548.
Steinhofel, K., A. Albrecht, and C.K.Wong. (2002). “Fast Parallel Heuristics for the Job Shop Scheduling Problem.” Computers & Operations Research 29, 151–169.
Wang, L. and D.Z. Zheng. (2001). “An Effective Hybrid Optimisation Strategy for Job-Shop Scheduling Problems.” Computers & Operations Research 28, 585–596.
Wong, S.Y.W. (2001). “Hybrid Simulated Annealing/Genetic Algorithm Approach to Short Term Hydro-Thermal Scheduling with Multiple Thermal Plants.” Electrical Power & Energy Systems 23, 565–575.
Van Laarhoven, P.J.M., E.H. Aarts, and J.K. Lenstra. (1992). “Job Shop Scheduling by Simulated Annealing.” Operations Research 40(1), 113–125.
Vales-Alonso, J., J. Fernandez, F.J. Gonzalez-Castano, and A. Cabarello. (2003). “A Parallel Optimization Approach for Controlling Allele Diversity in Conversation Schemes.” Mathematical Biosciences 183(2), 161–173.
Voogd, J.M., P.M.A. Sloot, and R. Dantzig. (1994). “Crystallization on a Shape.” Future Generation Computer Systems 10(2/3), 359–361.
Yong, L., K. Lishan, and D.J. Evans. (1995). “The Annealing Evolution Algorithm as Function Optimizer.” Parallel Computing 21(3), 389–400.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Aydin, M.E., Fogarty, T.C. A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems. Journal of Heuristics 10, 269–292 (2004). https://doi.org/10.1023/B:HEUR.0000026896.44360.f9
Issue Date:
DOI: https://doi.org/10.1023/B:HEUR.0000026896.44360.f9