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  • DANTE - The combination between an ant colony optimization algorithm and a depth search method
    Publication . Cardoso, Pedro J. S.; Jesus, Mário; Marquez, Alberto
    The 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 procedure
    Publication . Cardoso, Pedro J. S.; Jesus, Mário; Marquez, Alberto
    The 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.
  • Differential evolution in shortest path problems
    Publication . Guerreiro, Pedro; Jesus, Mário; Marquez, Alberto
    This paper proves that the Differential Evolution (DE) algorithm is valid to solve the Shortest Path (SP) problem in random, median sized networks. From the trials, we have obtained an 9% accuracy, in the worst case scenario.