Name
Abstract | ||
|---|---|---|
We introduce Group Learning for Ant Colony Optimization applied to combinatorial optimization problems with group-structured solution encodings. In contrast to the common assignment of one pheromone value per solution component in Group Learning each solution component has one pheromone value per group. Hence, the algorithm has the possibility to learn the optimal group membership of the components. We present different strategies for Group Learning and evaluate these in simulation experiments for the Vehicle Routing Problem with Time Windows using the problem instances of Solomon. We describe a revised Ant Colony System (ACS) algorithm which does not use a local pheromone update while maintaining the general ideas of ACS. We evaluate the revised ACS experimentally comparing it to the original ACS. Our experimental results show that Group Learning is a valuable modification for Ant Colony Optimization. Additionally, the results indicate that the revised ACS performs at least as well as the original algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2576768.2598214 | GECCO |
Keywords | Field | DocType |
combinatorial optimization,transportation,ant algorithms,heuristic methods,empirical study | Ant colony optimization algorithms,Vehicle routing problem,Combinatorial optimization problem,Computer science,Group learning,Combinatorial optimization,Artificial intelligence,Ant colony,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gunnar Völkel | 1 | 38 | 3.72 |
| Markus Maucher | 2 | 63 | 5.74 |
| Uwe Schöning | 3 | 998 | 105.69 |
| Hans A. Kestler | 4 | 468 | 44.88 |