A PSO-based memetic algorithm for the team orienteering problem

Authors:
Duc-Cuong Dang;Rym Nesrine Guibadj;Aziz Moukrim
Affiliations:
Université de Technologie de Compiègne, Heudiasyc, CNRS, UMR, Compiègne, France;Université de Technologie de Compiègne, Heudiasyc, CNRS, UMR, Compiègne, France and VEOLIA Transport, Paris, France;Université de Technologie de Compiègne, Heudiasyc, CNRS, UMR, Compiègne, France
Venue:
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Year:
2011

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Abstract

This paper proposes an effective Particle Swarm Optimization (PSO)-based Memetic Algorithm (PSOMA) for the Team Orienteering Problem (TOP). TOP is a particular vehicle routing problem whose aim is to maximize the profit gained from visiting clients while not exceeding a travel cost/time limit. Our PSOMA features optimal splitting techniques and genetic crossover operators. Furthermore, the memetic characteristic of our PSOMA is strengthened by an intensive use of local search techniques and also by a low value of 0.07 for inertia. In our experiments with the standard benchmark for TOP, PSOMA attained a gap of only 0.016%, as compared to 0.041%, the best known gap in the literature.