Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multicriteria Optimization
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Computation
DEMO: differential evolution for multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
We propose the use of rough sets theory to improve the first approximation provided by a multi-objective evolutionary algorithm and retain the nondominated solutions using a new adaptive grid based on the ε-dominance concept that tries to overcome the main limitation of ε-dominance: the loss of several nondominated solutions from the hypergrid adopted in the archive because of the way in which solutions are selected within each box. We decided to use a multi-objective version of differential evolution to build a first approximation of the Pareto front and in a second stage, we use the rough sets theory in order to improve the spread of the solutions found so far. To assess our proposed hybrid approach, we adopt a set of standard test functions and metrics taken from the specialized literature. Our results are compared with respect to the NSGA-II, which is an approach representative of the state-of-the-art in the area.