A Genetic Algorithm vs. Local Search Methods for Solving the Orienteering Problem in Large Networks

Abstract

AbstractThe 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.KeywordsGenetic AlgorithmLocal SearchGreedy Randomise Adaptive Search ProcedureLocal Search MethodDiscrete Particle Swarm OptimizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.