A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems

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.

[1]  T.C. Fogarty,et al.  A Distributed Resource Evolutionary Algorithm Machine (DREAM) , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[2]  Pierluigi Crescenzi,et al.  Parallel Simulated Annealing for Shape Detection , 1995, Comput. Vis. Image Underst..

[3]  M. Guignard.  A Lagrangean dual ascent algorithm for simple plant location problems , 1988 .

[4]  Kurt Jörnsten,et al.  Dual search procedures for the exact formulation of the simple plant location problem with spatial interaction , 1996 .

[5]  Ben Paechter,et al.  A scalable and robust framework for distributed applications , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Katsuhiko Takahashi,et al.  Simulated annealing approach for minimizing the makespan of the general job-shop , 1999 .

[7]  C.K. Wong,et al.  Two simulated annealing-based heuristics for the job shop scheduling problem , 1999, Eur. J. Oper. Res..

[8]  C.K. Wong,et al.  Fast parallel heuristics for the job shop scheduling problem , 2002, Comput. Oper. Res..

[9]  Jean-Luc Lutton,et al.  A Parallel Simulated Annealing Algorithm , 1993, Parallel Comput..

[10]  Peter M. A. Sloot,et al.  Crystallization on a sphere , 1994, Future Gener. Comput. Syst..

[11]  Ju-Jang Lee,et al.  Adaptive simulated annealing genetic algorithm for system identification , 1996 .

[12]  Daniel R. Greening,et al.  Parallel simulated annealing techniques , 1990 .

[13]  K. Al-Sultan,et al.  A Genetic Algorithm for the Set Covering Problem , 1996 .

[14]  John E. Beasley,et al.  Obtaining test problems via Internet , 1996, J. Glob. Optim..

[15]  Michael Kolonko,et al.  Some new results on simulated annealing applied to the job shop scheduling problem , 1999, Eur. J. Oper. Res..

[16]  David J. Evans,et al.  The Annealing Evolution Algorithm as Function Optimizer , 1995, Parallel Comput..

[17]  Ling Wang,et al.  An effective hybrid optimization strategy for job-shop scheduling problems , 2001, Comput. Oper. Res..

[18]  Gérard Cornuéjols,et al.  A projection method for the uncapacitated facility location problem , 1990, Math. Program..

[19]  Afonso Ferreira,et al.  BOUNDING THE PROBABILITY OF SUCCESS OF STOCHASTIC METHODS FOR GLOBAL OPTIMIZATION , 1993 .

[20]  Manfred Körkel.  On the exact solution of large-scale simple plant location problems , 1989 .

[21]  Donald Erlenkotter,et al.  A Dual-Based Procedure for Uncapacitated Facility Location , 1978, Oper. Res..

[22]  Suchendra M. Bhandarkar,et al.  Parallel Computing for Chromosome Reconstruction via Ordering of DNA Sequences , 1998, Parallel Comput..

[23]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[24]  Javier Vales-Alonso,et al.  A parallel optimization approach for controlling allele diversity in conservation schemes. , 2003, Mathematical biosciences.

[25]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[26]  M. Emin Aydin.  A MODULAR SIMULATED ANNEALING ALGORITHM FOR MULTI- AGENT SYSTEMS: A JOB-SHOP SCHEDULING APPLIACTION , 2002 .

[27]  Alessandro Bevilacqua,et al.  A Methodological Approach to Parallel Simulated Annealing on an SMP System , 2002, J. Parallel Distributed Comput..

[28]  Rajan Batta,et al.  On the use of genetic algorithms to solve location problems , 2002, Comput. Oper. Res..

[29]  Jozef Kratica,et al.  Solving the simple plant location problem by genetic algorithm , 2001, RAIRO Oper. Res..

[30]  Jeng-Shyang Pan,et al.  Vector quantization based on genetic simulated annealing , 2001, Signal Process..

[31]  Yuefan Deng,et al.  Parallel Simulated Annealing by Mixing of States , 1999 .

[32]  Hartmut Schmeck,et al.  Parallel implementations of evolutionary algorithms , 2001 .

[33]  Myunghwan Kim,et al.  Stepwise-overlapped parallel annealing and its application to floorplan designs , 1991, Comput. Aided Des..

[34]  Suzannah Yin Wa Wong,et al.  Hybrid simulated annealing/genetic algorithm approach to short-term hydro-thermal scheduling with multiple thermal plants , 2001 .

[35]  H. Simão,et al.  A dual simplex algorithm for the canonical representation of the uncapacitated facility location problem , 1989 .

[36]  J. Beasley.  Lagrangean heuristics for location problems , 1993 .

[37]  K. Holmberg,et al.  Experiments with primal - dual decomposition and subgradient methods for the uncapacitatied facility location problem , 2001 .

[38]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..