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- Cultural heritage visits supported on visitors' preferences and mobile devicesPublication . Cardoso, Pedro; Rodrigues, Joao; Pereira, Joao; Nogin, Sergey; Lessa, Joana; Ramos, Celia; Bajireanu, Roman; Gomes, Miguel; Bica, PauloMonuments, museums and cities are great places to feel and experience neat and interesting things. But cultural heritage is experienced differently by different visitors. The more erudite may know beforehand what they intend to explore, while the least literate usually know and are capable of expressing some of their preferences but do not exactly realize what to see and explore. This paper proposes the use of a mobile application to set an itinerary where you can move at your own pace and, at the same time, have all the complementary information you need about each of the points of interest. The application is designed in face of an adaptive user interface where the routing and augmented reality are connected to acknowledge the needs of different user categories, such as elders, kids, experts or general users
- DANTE - The combination between an ant colony optimization algorithm and a depth search methodPublication . Cardoso, Pedro J. S.; Jesus, Mário; Marquez, AlbertoThe ε-DANTE method is an hybrid meta-heuristic. In combines the evolutionary Ant Colony Optimization (ACO) algorithms with a limited Depth Search. This Depth Search is based in the pheromone trails used by the ACO, which allows it to be oriented to the more promising areas of the search space. Some results are presented for the multiple objective k-Degree Spanning Trees problem, proving the effectiveness of the method when compared with other already tested evolutionary methods. © 2008 IEEE.
- epsilon-DANTE: an ant colony oriented depth search procedurePublication . Cardoso, Pedro J. S.; Jesus, Mário; Marquez, AlbertoThe epsilon-Depth ANT Explorer (epsilon-DANTE) algorithm applied to a multiple objective optimization problem is presented in this paper. This method is a hybridization of the ant colony optimization algorithm with a depth search procedure, putting together an oriented/limited depth search. A particular design of the pheromone set of rules is suggested for these kinds of optimization problems, which are an adaptation of the single objective case. Six versions with incremental features are presented as an evolutive path, beginning in a single colony approach, where no depth search is applied, to the final epsilon-DANTE. Versions are compared among themselves in a set of instances of the multiple objective Traveling Salesman Problem. Finally, our best version of epsilon-DANTE is compared with several established heuristics in the field showing some promising results.