A genetic algorithm vs. local search methods for solving the orienteering problem in large networks

Authors:
Joanna Karbowska-Chilińska;Paweł Zabielski
Affiliations:
Faculty of Computer Science, Bialystok University of Technology, Poland;Faculty of Computer Science, Bialystok University of Technology, Poland
Venue:
KES'12 Proceedings of the 16th international conference on Knowledge Engineering, Machine Learning and Lattice Computing with Applications
Year:
2012

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Abstract

The Orienteering problem (OP) can be modelled as a weighted graph with set of vertices where each has a score. The main OP goal is to find a route that maximises the sum of scores, in addition the length of the route not exceeded the given limit. In this paper we present our genetic algorithm (GA) with inserting as well as removing mutation solving the OP. We compare our results with other local search methods such as: the greedy randomised adaptive search procedure (GRASP) (in addition with path relinking (PR)) and the guided local search method (GLS). The computer experiments have been conducted on the large transport network (908 cities in Poland). They indicate that our algorithm gives better results and is significantly faster than the mentioned local search methods.