Abstract
Greedy randomized adaptive search procedure (GRASP) is a metaheuristic framework which has been extensively used for solving a wide variety of hard combinatorial optimization problems. Several diversity maximization problems have considered GRASP either as the main metaheuristic or even as a part of a hybrid algorithm, mainly due to its versatility to be adapted to any optimization problem. This chapter is focused on reviewing the most recent works considering GRASP for maximizing diversity and proposing a basic design and implementation of GRASP in the context of diversity problems. The resulting design is evaluated over the MDPLIB 2.0, which has become a de facto standard test bed for this family of problems.
Jesús Sánchez-Oro
Associate Professor
Associate Professor at the Computer Science Department, being one of the senior researchers of the Group for Research on Algorithms For Optimization GRAFO.
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.