Self Learning Strategy for the Team Orienteering Problem (SLS-TOP)

The Team Orienteering Problem (TOP) can be viewed as a combination of both vehicle routing and knapsack problems, where its goal is to maximize the total gained profit from the visited customers (without imposing the visit of all customers). In this paper, a self learning strategy is considered in order to tackle the TOP, where information provided from local optima are used to create new solutions with higher quality. Efficient deep searching (intensification) and jumping strategy (diversification) are combined. A number of instances, extracted from the literature, are tested with the proposed method. As shown in the experimental part, one of the main achievement of the method is its ability to match all best bounds published in the literature by using a considerably smaller CPU/time. Then, for the first preliminary study using both jumping self learning strategies, encouraging results have been obtained. We hope that a hybridation with a black-box solver, like Cplex or Gurobi, can be considered as the main future of the method for finding new bounds, especially for large-scale instances.