Examinando por Autor "Caballero, H."

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  • Miniatura
    ÍtemAcceso Abierto
    A bi-objetive cat swarm optimization algorithm for set covering problem
    (Springer Verlag, 2016) Crawford, B.; Soto, R.; Caballero, H.; Olguín, E.
    In this paper, we study a classical problem in combinatorics and computer science, Set Covering Problem. It is one of Karp’s 21 NP-complete problems, using a new and original metaheuristic, Cat Swarm Optimization. This algorithm imitates the domestic cat through two states: seeking and tracing mode. The OR-Library of Beasley instances were used for the benchmark with additional fitness function, thus the problem was transformed from Mono-objective to Bi-objective. The Cat Swarm Optimization finds a set solution non-dominated based on Pareto concepts, and an external file for storing them. The results are promising for further continue in future work optimizing this problem. © Springer International Publishing Switzerland 2016.
  • Miniatura
    ÍtemAcceso Abierto
    Solving biobjective set covering problem using binary cat swarm optimization algorithm
    (Springer Verlag, 2016) Crawford, B.; Soto, R.; Caballero, H.; Olguín, E.; Misra, S.
    The set cover problem is a classical question in combinatorics, computer science and complexity theory. It is one of Karp’s 21 NP-complete problems shown to be NP-complete in 1972. Several algorithms have been proposed to solve this problem, based on genetic algorithms (GA), Particle Swarm Optimizer (PSO) and in recent years algorithms based in behavior algorithms based groups or herds of animals, such as frogs, bats, bees and domestic cats. This work presents the basic features of the algorithm based on the behavior of domestic cats and results to solve the SCP bi-objective, experimental results and opportunities to improve results using adaptive techniques applied to Cat Swarm Optimization. For this purpose we will use instances of SCP OR-Library of Beasley by adding an extra function fitness to transform the Beasly instance to Bi-Objective problem. © Springer International Publishing Switzerland 2016.