TY - CONF
T1 - A Study into Ant Colony Optimization, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems
T2 - Springer Lecture Notes on Computer Science
Y1 - 2004
A1 - van Hemert, J. I.
A1 - Solnon, C.
ED - J. Gottlieb
ED - G. Raidl
KW - ant colony optimisation
KW - constraint programming
KW - constraint satisfaction
KW - evolutionary computation
AB - We compare two heuristic approaches, evolutionary computation and ant colony optimisation, and a complete tree-search approach, constraint programming, for solving binary constraint satisfaction problems. We experimentally show that, if evolutionary computation is far from being able to compete with the two other approaches, ant colony optimisation nearly always succeeds in finding a solution, so that it can actually compete with constraint programming. The resampling ratio is used to provide insight into heuristic algorithms performances. Regarding efficiency, we show that if constraint programming is the fastest when instances have a low number of variables, ant colony optimisation becomes faster when increasing the number of variables.
JF - Springer Lecture Notes on Computer Science
PB - Springer-Verlag, Berlin
SN - 3-540-21367-8
ER -