Abstract
The study of efficient metaheuristics to solve optimization problems has been the subject of much research by the scientific community. To obtain good results in terms of solution quality and computational time it is important to have a good configuration of the metaheuristic. This process of specifying control flow and parameter values of a method is a hard task. Thus, this project has as main idea the development and improvement of the adaptive Biased Random-key Genetic Algorithm (A-BRKGA) method to choose which components will be used and in which sequence (A-BRKGA flow) and which parameters to use while an instance of a problem is being solved. To this end, machine learning techniques and adaptive and reactive mechanisms will be studied to construct an A-BRKGA with online configuration of parameters and control flow. The goal is to generate an efficient algorithm to solve combinatorial optimization problems and make the code easy to reuse. In order to evaluate the proposed method, four optimization problems with industrial and logistical applications will be studied: field technician scheduling problem, multicommodity traveling salesman problem with priority prizes, two-stage capacitated facility location problem, and facility location problem with overlapping. The computational tests will use test problems available in the literature and real case studies. The method will be compared with state-of-the-art algorithms through statistical analysis. (AU)
| Articles published in Agência FAPESP Newsletter about the research grant: |
| TITULO |
| Articles published in other media outlets (0 total): |
| More itemsLess items |
| VEICULO: TITULO (DATA) |
| VEICULO: TITULO (DATA) |