Integrating ACO and Constraint Propagation
Ant Colony Optimisation algorithms perform competitively with other meta-heuristics for many types of optimisation problems, but unfortunately their performance does not always degrade gracefully when the problem contains hard constraints. Many industrially relevant problems, such as fleet routing, rostering and timetabling, are typically subject to hard constraints. A complementary technique for solving combinatorial optimisation problems is Constraint Programming (CP). CP techniques are specialized for solving hard constraints, but they may be inefficient as an optimisation method if the feasible space is very large. A hybrid approach combining both techniques therefore holds the hope to combine these complementary advantages. The paper explores how such an integration can be achieved and presents a hybrid search method CPACS derived by embedding CP into ACS. We have tested CPACS on job scheduling problems. Initial benchmark results are encouraging and suggest that CPACS has the biggest advantage over the individual methods for problems of medium tightness, where the constraints cause a highly fragmented but still very large search space.
KeywordsFeasible Solution Constraint Programming Constraint Propagation Hard Constraint Constraint Solver
Unable to display preview. Download preview PDF.
- 2.Barnier, N., Brisset, P.: Combine & conquer: Genetic algorithm and CP for optimization. In: Principles and Practice of Constraint Programming, Pisa (October 1998)Google Scholar
- 3.Bauer, A., Bullnheimer, B., Hartl, R.F., Strauss, C.: An ant colony optimization approach for the single machine total tardiness problem. In: Proceedings of the Congress on Evolutionary Computation, Washington/DC (July 1999)Google Scholar
- 5.Blum, C., Sampels, M.: When model bias is stronger than selection pressure. In: Parallel Problem Solving From Nature (PPSN-VII), Granada (September 2002)Google Scholar
- 6.Carlsson, M., Ottosson, G., Carlson, B.: An open-ended finite domain constraint solver. In: Proc. PLILP 1997 Programming Languages: Implementations, Logics, and Programs, Southampton (September 1997)Google Scholar
- 9.den Besten, M., Stützle, T., Dorigo, M.: Ant colony optimization for the total weighted tardiness problem. In: Parallel Problem Solving from Nature - PPSN VI, Paris, France (September 2000)Google Scholar
- 12.Focacci, F., Laburthe, F., Lodi, A.: Local search and constraint programming. In: Glover, F., Kochenberger, G. (eds.) Handbook of metaheuristics, Kluwer, Boston (2003)Google Scholar
- 17.Socha, K.: MAX-MIN ant system for international timetabling competition. Technical report, Universite Libre de Bruxelles (September 2003) TR/IRIDIA/2003-30Google Scholar