Programa de Pós Graduação em Engenharia Mineral, Universidade Federal de Ouro Preto, Departamento de Engenharia de Minas, Campus Universitário, s/n, Morro do Cruzeiro, 35400-000 Ouro Preto, MG, Brazil.
Departamento de Ciências Exatas, Universidade do Estado de Minas Gerais, Av. Brasília, 1304, Baú, 35930-314 João Monlevade, MG, Brazil.
An Acad Bras Cienc. 2021 Sep 24;93(4):e20201242. doi: 10.1590/0001-3765202120201242. eCollection 2021.
This paper proposes the use of a hybrid method that combines Biased Random Key Genetic Algorithm (BRKGA) with a local search heuristic to separate Brazilian tailing dam data into groups. The goal was identifying dams similar to Fundão and B1 failed dams. The groups were created by solving the clustering problem by BRKGA. The clustering problem consists in separating a set of objects into groups such that members of each group are similar to each other. The data was composed by 427 dams, with the actual 425 dams of Brazilian Register of Tailing Dams and the two Brazilian failed dams from the last years. Computational experiments considering real data available are presented to demonstrate the efficacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identification of tailings dams with risk potentials, assisting in the identification of these dams.
本文提出了一种混合方法，该方法结合了有偏差随机键遗传算法（BRKGA）和局部搜索启发式算法，以将巴西尾矿坝数据分为几组。目标是识别与 Fundão 和 B1 失事尾矿坝相似的尾矿坝。通过 BRKGA 解决聚类问题来创建组。聚类问题是将一组对象分成组，使得每个组的成员彼此相似。该数据由 427 座大坝组成，其中包括巴西尾矿坝登记册中的 425 座实际大坝和过去几年中巴西的两座失事大坝。本文提出了考虑实际可用数据的计算实验，以证明所提出方法产生可行解的有效性。因此，预计这些好的结果可以应用于识别有风险潜力的尾矿坝，有助于识别这些尾矿坝。
