Abstract
Meta-heuristic search algorithms, by their very nature, are applicable across a range of optimisation problems. In practice however, meta-heuristics have been tailored to solve particular problems. Recent work by Randall and Abramson (2001b)has successfully shown that iterative meta-heuristics, such as simulated annealing and tabu search, can be successfully generalised to solve a range of problems without modification though the use of a uniform representation language. Constructive meta-heuristics, such as ant colony optimisation and generalised random adaptive search procedures, pose more substantial problems to achieve this same level of generalistaion. This paper investigates the issues involved and suggests some measures by which generalisation could be achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abramson, D. and Randall, M. A Simulated Annealing Code for General Integer Linear Programs. Annals of Operations Research 1999; 86: 3–21.
Brooke, A., Kendrick, D., Meeraus, A. and Raman, R. GAMS Language Guide, GAMS Development Corporation, 1997.
Burkard, R. Quadratic Assignment Problems. European Journal of Operational Research 1984; 15:283–289.
Dorigo, M. and Gambardella, L. Ant Colony System: A Cooperative Learning Approach to the Travelling Salesman Problem. IEEE Transactions on Evolutionary Computation 1997; 1:53–66.
Dorigo, M., Maniezzo, V. and Colomi, A. The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man and Cybernetics — Part B 1997; 26: 29–41.
Feo, T. and Resende, M. Greedy Randomized Adaptive Search Procedures. Journal of Global Optimization 1995; 51: 109–133.
Fourer, R., Gay, D. and Kernighan, B. AMPL: A Modeling Language for Mathematical Programming. California: Duxbury Press, 1993.
Glover, F. and Laguna, M. Tabu Search. Boston: Kluwer Academic, 1997.
Gutjahr, W. A Graph-based Ant System and its Convergence. Future Generation Computer Systems 2000; 16: 873–888.
Lawler, E., Lenstra, J., Rinnoy Kan, A. and Shmoys, D. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Chichester: Wiley, 1985.
Mavridou, T., Pardalos, P., Pitsoulis, L. and Resende, M. (1995) “Parallel Search for Combinatorial Optimization: Genetic Algorithms, Simulated Annealing, Tabu Search and GRASP”, Lecture Notes In Computer Science, 980, Ferreira, A. and Rolim, J. ed. Berlin: Springer-Verlag, 1995.
Michalewicz, Z. and Fogel, D. How to Solve it: Modern Heuristic, Berlin: Springer Verlag, 2000.
Ramalhinho, H. and Serra, D. Adaptive Approach Heuristics for the Generalized Assignment Problem, Working Paper Series, No. 288, UPF, 1998.
Randall, M. “A General Modelling Systems and Meta-heuristic based Solver for Combinatorial Optimisation Problems”, PhD thesis, Faculty of Environmental Science, Griffith University, 1999.
Randall, M. “The Use of Feasibility Restoration in Iterative Meta-heuristics for Solving Combinatorial Optimisation Problems”. Working Paper, 2001.
Randall, M. and Abramson, D. (2001) A General Meta-heuristic Based Solver for Combinatorial Optimisation Problems. Journal of Computational Optimisation and Applications 2001; 20 (to appear).
Randall, M. and Montgomery, J. “Candidate Set Strategies for Ant Colony Optimisation”. Working paper, 2001.
Randall, M., McMahon, G. and Sugden, S. A Simulated Annealing Approach to Communication Network Design. Journal of Combinatorial Optimization (2001 to appear).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media New York
About this chapter
Cite this chapter
Randall, M. (2002). A General Framework for Constructive Meta-Heuristics. In: Kozan, E., Ohuchi, A. (eds) Operations Research/Management Science at Work. International Series in Operations Research & Management Science, vol 43. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0819-9_7
Download citation
DOI: https://doi.org/10.1007/978-1-4615-0819-9_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5254-9
Online ISBN: 978-1-4615-0819-9
eBook Packages: Springer Book Archive