Automated Parameterisation of a Metaheuristic for the Orienteering Problem
نویسندگان
چکیده
Developing metaheuristics requires in general a lot of work tuning different parameters. This paper presents a two–level algorithm to tackle this problem: an upper–level algorithm is used to determine the most appropriate set of parameters for a lower–level metaheuristic. This approach is applied to an Ant Colony Optimisation (ACO) metaheuristic that was designed to solve the Orienteering Problem (OP). That is a particular routing problem in which a score is earned for visiting a location. The objective is to maximise the sum of the scores, while not exceeding a given time budget. The ACO algorithm uses a set of ants that communicate through the environment by means of a pheromone trail. The transition rule and pheromone updating rules are influenced by a large number of parameters. These parameters are fine–tuned by a Genetic Algorithm (GA), which trains the ACO using test problems from the literature. The resulting ACO algorithm is compared with an exact algorithm by applying it to another set of problems. The scores obtained by the resulting algorithm are very near the optimal scores for the test problems.
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