Multi-Objective Artificial Bee Colony algorithm applied to the bi-objective orienteering problem

Abstract We propose a new evolutionary computation approach for solving the multi-objective orienteering problem. This problem has applications in different fields like routing problems or logistic problems. In our case, the final motivation is the design of individual tourist routes. The tourists have different priorities about points of interests grouped into categories (for example, cultural or leisure), so, a multi-objective solution system is needed. In order to obtain the best Pareto solutions, the Artificial Bee Colony algorithm (based on swarm intelligence) has been adapted to the multi-objective context. The performance of this approach has been compared with two previous algorithms from the literature for the bi-objective orienteering problem (P-ACO and P-VNS), in benchmark instances and real-world instances. The results indicate that this new approach is good for solving the multi-objective orienteering problem.

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