@article{Brito_Fadel_Semaan_Montenegro_2021, title={Heuristics Applied to Minimization of the Maximum-Diameter Clustering Problem}, volume={19}, url={https://latamt.ieeer9.org/index.php/transactions/article/view/3848}, abstractNote={<p>This paper introduces two heuristic algorithms for the Maximum-Diameter Clustering Problem (MDCP), based on the Biased Random-Key Genetic Algorithm (BRKGA) and the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristics. This problem consists of finding k clusters that minimize the largest within-cluster distance (diameter) among all clusters. The MDCP is classified as NP-hard and presents increased difficulty when attempting to determine the optimal solution for any instance. The results obtained in the experiments using 50 well-known instances indicate a good performance of proposed heuristics, that outperformed both three algorithms and an integer programming model from the literature.</p>}, number={4}, journal={IEEE Latin America Transactions}, author={Brito, José André de Moura and Fadel, Augusto Cesar and Semaan, Gustavo Silva and Montenegro, Flávio}, year={2021}, month={Jun.}, pages={652–659} }