Catálogo de publicaciones - libros

The maximum diversity problem (MDP) consists in identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics among themselves. The identification of such solution is an NP-hard problem. In this work, we propose a hybrid GRASP metaheuristic for the MDP that incorporates a data mining process. Data mining refers to the extraction of new and potentially useful knowledge from datasets in terms of patterns and rules. We believe that data mining techniques can be used to extract patterns that represent characteristics of sub-optimal solutions of a combinatorial optimization problem. Therefore these patterns can be used to guide the search for better solutions in metaheuristics procedures. Performance comparison between related work and the proposed hybrid heuristics is provided. Experimental results show that the new hybrid GRASP is quite robust and, mainly, this strategy is able to find high-quality solutions in less computational time.

In this paper we introduce the new hybrid Particle Swarm Optimization algorithm for multi-objective optimization ClustMPSO. We combined the PSO algorithm with clustering techniques to divide all particles into several subswarms. Strategies for updating the personal best position of a particle, for selection of the neighbourhood best and for swarm dominance are proposed. The algorithm is analyzed on both artificial optimization functions and on an important real world problem from biochemistry. The molecule docking problem is to predict the three dimensional structure and the affinity of a binding of a target receptor and a ligand. ClustMPSO clearly outperforms a well-known Lamarckian Genetic Algorithm for the problem.

The p – hub median problem is an NP hard location – allocation problem, that consists of finding p points to establish facilities and the assignment of the users to these points. In the capacitated version of this problem, each hub has got a maximum capacity limiting the traffic to be assigned. A new evolutionary approach that has been very effective for solving optimization problems is Path Relinking , an extension of Scatter Search that links solutions over neighborhood spaces. GRASP is a well-known randomized multistart metaheuristic. In this paper, we present a hybrid GRASP-Path Relinking for the capacitated p – hub median problem where the GRASP is used to construct the population of the Path Relinking . Computational results demonstrate that the hybrid GRASP-Path Relinking provides better solutions, in terms of both running times and solution quality.