Resumen
Variable neighborhood descent (VND) is a local search-based heuristic which performs a deterministic exploration of a fixed set of neighborhood structures. Its success is based on the simple fact that different neighborhood structures usually have different local minimum. Thus, the local optima trap problem may be eventually solved by deterministic change of neighborhoods. VND can be used as a local search routine, and, therefore, it could be used within other metaheuristics. In this chapter we discuss typical problems that arise when designing VND: what neighborhood structures could be used, what would be their order, what rule of their change during the search would be used, etc. Comparative analysis of usual sequential VND variants is performed considering the traveling salesman problem.
Abraham Duarte
Catedrático de Universidad
Mi carrera investigadora se ha centrado en el desarrollo de nuevos algoritmos y técnicas de Inteligencia Computacional (metaheurísticas) y su aplicación a diferentes problemas en Ciencia e Ingeniería desde que me incorporé a la Universidad Rey Juan Carlos (URJC) en el octubre del año 2000.
Jesús Sánchez-Oro
Profesor Titular de Universidad
Profesor Titular del Departamento de Informática, siendo uno de los investigadores principales del Grupo de Investigación de Algoritmos para la Optimización GRAFO.