Graph layout problems are a family of combinatorial optimization problems that consist of finding an embedding of the vertices of an input graph into a host graph such that an objective function is optimized. Within this family of problems falls the so-called Two-Dimensional Bandwidth Minimization Problem (2DBMP). The 2DBMP aims to minimize the maximum distance between each pair of adjacent vertices of the input graph when it is embedded into a grid host graph. In this paper, we present an efficient heuristic algorithm based on the Iterated Greedy (IG) framework hybridized with a new local search strategy to tackle the 2DBMP. Particularly, we propose different designs for the main IG procedures (i.e., construction, destruction, and reconstruction) based on the trade-off between intensification and diversification. Additionally, the improvement method incorporates three advanced strategies: an efficient way to evaluate the objective function of neighbor solutions, a tiebreak criterion to deal with “flat landscapes”, and a neighborhood reduction technique. Extensive experimentation was carried out to assess the IG performance over state-of-the-art methods, emerging our approach as the most competitive algorithm. Specifically, IG finds the best solutions for all instances considered in considerably less execution time. Statistical tests corroborate the merit of our proposal.
European Journal of Operational Research
Artificial Intelligence Phd Student
Sergio Cavero was born Madrid (Spain) on September 24, 1997. He graduated in Software Engineering from Universidad Politécnica de Madrid in 2019. During his undergraduate studies he made a stay at the University of Bradford (UK). In addition, he was awarded twice with the ‘Beca de Excelencia of the Comunidad de Madrid, and also awarded for the Best Final Degree Project. Later, he completed a Master’s Degree in Artificial Intelligence at the same university (UPM) obtaining awards for Best Academic Record (‘Premio José Cuena’) and Best Master’s Thesis. He academic results lend him be beneficiary of one of the ‘Ayudas Para la Formación de Profesorado Universitario (FPU)’, funded by the Spanish Government. He is currently carrying out his doctoral thesis at the Universidad Rey Juan Carlos, supervised by Professors Abraham Duarte and Eduardo G. Pardo. His main research interests focus on the interface among Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristics procedures for optimization problems modeled by graphs.
Abraham Duarte is Full Professor in the Computer Science Department at the Rey Juan Carlos University (Madrid, Spain). He has done extensive research in the interface between computer science, artificial intelligence, and operations research to develop solution methods based on Computational Intelligence (metaheuristics) for practical problems in operations-management areas such as logistics and supply chains, telecommunications, decision-making under uncertainty and optimization of simulated systems.