Approaching rank aggregation problems by using evolution strategies: The case of the optimal bucket order problem

The optimal bucket order problem consists in obtaining a complete consensus ranking (ties are allowed) from a matrix of preferences (possibly obtained from a database of rankings). In this paper, we tackle this problem by using evolution strategies. We designed specific mutation operators which are able to modify the inner structure of the buckets, which introduces more diversity into the search process. We also study different initialization methods and strategies for the generation of the population of descendants. The proposed evolution strategies are tested using a benchmark of 52 databases and compared with the current state-of-the-art algorithm LIA. We carry out a standard machine learning statistical analysis procedure to identify a subset of outstanding configurations of the proposed evolution strategies. The study shows that the best evolution strategy improves upon the accuracy obtained by the standard greedy method (BPA) by 35%, and that of LIA by 12.5%

GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs

We present a prototype of a software tool for exploration of multiple combinatorial optimisation problems in large real-world and synthetic complex networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial Explorer), provides a unified framework for scalable computation and presentation of high-quality suboptimal solutions and bounds for a number of widely studied combinatorial optimisation problems. Efficient representation and applicability to large-scale graphs and complex networks are particularly considered in its design. The problems currently supported include maximum clique, graph colouring, maximum independent set, minimum vertex clique covering, minimum dominating set, as well as the longest simple cycle problem. Suboptimal solutions and intervals for optimal objective values are estimated using scalable heuristics. The tool is designed with extensibility in mind, with the view of further problems and both new fast and high-performance heuristics to be added in the future. GraphCombEx has already been successfully used as a support tool in a number of recent research studies using combinatorial optimisation to analyse complex networks, indicating its promise as a research software tool

Problemas de Optimización Decepcionantes Dinámicos, experimentación con Metaheurísticas

Dentro del campo de la optimización existen una serie de problemas llamados NP, que son aquellos que pueden ser resueltos por un algoritmo no determinístico en un tiempo polinomial de resolución. Debido a que el mundo real no es estático, sino dinámico, se crea la necesidad de acercar dichos problemas de prueba a la realidad, de ahí que surgieran los problemas de optimización dinámicos (PODs). Uno de los problemas clásicos de optimización que existen, son los problemas decepcionantes o engañosos, que son problemas de prueba de generación binaria a partir de XOR. Son llamados engañosos porque a los algoritmos les cuesta mucho obtener mejoras, ya que cuando se mejora la solución con una heurística se empeora la evaluación en la función objetivo. En los últimos años ha habido un creciente interés por la modelación de los problemas dinámicos de optimización y su solución con los algoritmos metaheurísticos. Por ello, el objetivo de esta investigación es analizar el comportamiento de las metaheurísticas clásicas frente a los problemas decepcionantes dinámicos, específicamente con cinco funciones decepcionantes y evaluando el rendimiento de los algoritmos aplicando test estadísticos no paramétricos. Además se realiza una comparación de los resultados del mejor algoritmo con dos algoritmos del estado de arte para resolver problemas de optimización dinámicos: Adaptive Hill Climbing Memetic Algorithm y Self Organized Random Immigrants Genetic Algorithm