Examinando por Autor "Norero E."

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    A binary cat swarm optimization algorithm for the Non-Unicost set covering problem
    (Hindawi Publishing Corporation, 2015) Crawford B.; Berríos N.; Johnson F.; Paredes F.; Castro C.; Soto R.; Norero E.
    The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library. © 2015 Broderick Crawford et al.
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    ÍtemAcceso Abierto
    A hybrid alldifferent-tabu search algorithm for solving sudoku puzzles
    (Hindawi Publishing Corporation, 2015) Soto, R.; Crawford, B.; Galleguillos C.; Paredes F.; Norero E.
    The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n2 × n2 grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n2. Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods. © 2015 Ricardo Soto et al.
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    ÍtemAcceso Abierto
    An artificial bee colony algorithm for the resource contrained project scheduling problem
    (Springer Verlag, 2015) Crawford, B.; Soto, R.; Johnson, F.; Norero E.; Olguín, E.
    We present an approach to solve the Resource Constrained Project Scheduling Problem. This problem consists on executing a group of activities limited by constraints. Precedence relationships force to some activities to begin after the finalization of others. In addition, processing every activity requires a predefined amount of limited resources. The target of this problem is to minimize the duration of whole project. In this paper, an approach based on Artificial Bee Colony algorithm for the Resource Constrained Project Scheduling Problem is presented. That algorithm is one of the most recent algorithms in the domain of the collective intelligence who was motivated by the intelligent behavior observed in the domestic bees to take the process of forage. Thus, ABC combines methods of local search and global search, trying to balance the process of the exploration and exploitation of the space of search. © Springer International Publishing Switzerland 2015.