Abstract
Graph Layout Problems refer to a family of optimization problems where the aim is to assign the vertices of an input graph to the vertices of a structured host graph, optimizing a certain objective function. In this paper, we tackle one of these problems, named Cyclic Antibandwidth Problem, where the objective is to maximize the minimum distance of all adjacent vertices, computed in a cycle host graph. Specifically, we propose a General Variable Neighborhood Search which combines an efficient Variable Neighborhood Descent with a novel destruction–reconstruction shaking procedure. Additionally, our proposal takes advantage of two new exploration strategies for this problem: a criterion for breaking the tie of solutions with the same objective function and an efficient evaluation of neighboring solutions. Furthermore, two new neighborhood reduction strategies are proposed. We conduct a thorough computational experience by comparing the algorithm proposed with the current state-of-the-art methods over a set of previously reported instances. The associated results show the merit of the introduced algorithm, emerging as the best performance method in those instances where the optima are unknown. These results are further confirmed with nonparametric statistical tests.
Sergio Cavero
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.
Eduardo García Pardo
Associate Professor
Miembro fundador del grupo de investigación GRAFO, cuya línea de investigación principal es el desarrollo de algoritmos para abordar problemas de optimización, temática sobre la que versa la Tesis Doctoral del investigador y en la que se enmarcan sus publicaciones más destacadas.
Abraham Duarte
Full Professor
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.