A PSO-based memetic algorithm for the team orienteering problem

Duc-Cuong Dang (1) , Rym Nesrine Guibadj (1, 2) , Aziz Moukrim (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 VEOLIA Transport, MERCUR subsidiary

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.

Mots clés: Swarm Intelligence; Metaheuristics; Team Orienteering Problem; Optimal Split

Domaines: Recherche opérationnelle [math.OC]

Duc-Cuong Dang, Rym Nesrine Guibadj, Aziz Moukrim. A PSO-based memetic algorithm for the team orienteering problem. EvoApplications 2011, Apr 2011, Turin, Italy. pp.471-480. ⟨hal-00575903⟩