Name
Abstract | ||
|---|---|---|
Scalarizing functions play a crucial role in multi-objective evolutionary algorithms MOEAs based on decomposition and the R2 indicator, since they guide the population towards nearly optimal solutions, assigning a fitness value to an individual according to a predefined target direction in objective space. This paper presents a general review of weighted scalarizing functions without constraints, which have been proposed not only within evolutionary multi-objective optimization but also in the mathematical programming literature. We also investigate their scalability upï¾źto 10 objectives, using the test problems of Lamé Superspheres on the MOEA/D and MOMBI-II frameworks. For this purpose, the best suited scalarizing functions and their model parameters are determined through the evolutionary calibrator EVOCA. Our experimental results reveal that some of these scalarizing functions are quite robust and suitable for handling many-objective optimization problems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-54157-0_34 | EMO |
Field | DocType | Citations |
Population,Mathematical optimization,Evolutionary algorithm,Artificial intelligence,Optimization problem,Mathematics,Scalability | Conference | 3 |
PageRank | References | Authors |
0.37 | 38 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miriam Pescador-Rojas | 1 | 3 | 0.37 |
| Raquel Hernández Gómez | 2 | 3 | 0.37 |
| Elizabeth Montero | 3 | 69 | 10.14 |
| Nicolás Rojas-Morales | 4 | 5 | 2.41 |
| María Cristina Riff | 5 | 17 | 6.72 |
| C. A. Coello Coello | 6 | 5799 | 427.99 |