Resumen

MEXICANO SANTOYO, Adriana; PEREZ ORTEGA, Joaquín; REYES SALGADO, Gerardo  y  ALMANZA ORTEGA, Nelva Nely. Characterization of Difficult Bin Packing Problem Instances Oriented to Improve Metaheuristic Algorithms. Comp. y Sist. [online]. 2015, vol.19, n.2, pp.295-308. ISSN 2007-9737.  https://doi.org/10.13053/CyS-19-2-1546.

This work presents a methodology for characterizing difficult instances of the Bin Packing Problem using Data Mining. Characteristics of such instances help to provide ideas for developing new strategies to find optimal solutions by improving the current solution algorithms or developing new ones. According to related work, in general, instance characterization has been used to make prediction of the algorithm that best solves an instance, or to improve one by associating the instance characteristics and performance of the algorithm that solves it. However, this work proposes the development of efficient solution algorithms guided by previous identification of characteristics that represent a greater impact on the difficulty of the instances. To validate our approach, we used a set of 1,615 instances, 6 well-known algorithms of the Bin Packing Problem, and 27 initial metrics. After applying our approach, 5 metrics were found relevant; these metrics helped to characterize 4 groups containing instances that could not be solved by any of the algorithms used in this work. Based on the gained knowledge from instance characterization, a new reduction method that helps to reduce the search space of a metaheuristic algorithm was proposed. Experimental results show that application of the reduction method allows finding more optimal solutions than those of best metaheuristics reported in the specialized literature.

Palabras llave : Characterization; clustering; metaheuristics; bin packing problem; reduction; knowledge discovery.

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