Network robustness against cascades is a major topic in the fields of complex networks. In this paper, we propose an attack-cost-based cascading failure model, where the attack cost of nodes is positively related to its degree. We compare four attacking strategies: the random removal strategy (RRS), the low-degree removal strategy (LDRS), the high-degree removal strategy (HDRS) and the genetic algorithm removal
strategy (GARS). It is shown that the network robustness against cascades is heavily affected by attack costs and the network exhibits the weakest robustness under GARS. We also explore the relationship
between the network robustness and tolerance parameter under these attacking strategies. The simulation results indicate that the critical value of tolerance parameter under GARS is greatly larger than that of other attacking strategies. Our work can supply insight into the robustness and vulnerability of complex networks corresponding to cascading failures.
Bautista, J.; Alfaro, R.; Batalla, C. International Journal of Computational Intelligence Systems Vol. 9, num. 4, p. 788-799 DOI: 10.1080/18756891.2016.1204125 Data de publicació: 2016-06-28 Article en revista
We aim at maximizing the comfort of operators in mixed-model assembly lines. To achieve this goal, we evaluate two assembly line balancing models: the first that minimizes the maximum ergonomic risk and the second one that minimizes the average absolute deviations of ergonomic risk. Through a case study we compare the results of the two models by two different resolution procedures: the Mixed Integer Linear Programming (MILP) and Greedy Randomized Adaptive Search Procedures (GRASP). Although linear programming offers best solution, the results given by GRASPs are competitive.
González del Campo, R.; Garmendia, L.; Recasens, J. International Journal of Computational Intelligence Systems Vol. 6, num. 4, p. 648-657 DOI: 10.1080/18756891.2013.802117 Data de publicació: 2013-07 Article en revista
In this paper are introduced some concepts of interval-valued fuzzy relations and some of their properties: reflexivity, symmetry, T-transitivity, composition and local reflexivity. The existence and uniqueness of T-transitive closure of interval-valued fuzzy relations is proved. An algorithm to compute the T-transitive closure of finite interval-valued fuzzy relations is showed. Some properties and some examples is given for t-representable and t-pseudo representable generalized t-norms.
Support vector machines (classification and regression) are powerful machine learning techniques for crisp data. In this paper, the problem is considered for interval data. Two methods to deal with the problem using support vector regression are proposed and two new methods for evaluating performance for estimating prediction interval are presented as well.