Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
The R Book
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Towards a quick computation of well-spread pareto-optimal solutions
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
DEMO: differential evolution for multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
This work explores the influence of three different dominance criteria, namely the Pareto-, epsilon-, and cone epsilon-dominance, on the performance of multiobjective evolutionary algorithms. The approaches are incorporated into two different algorithms, which are then applied to the solution of twelve benchmark problems from the ZDT and DTLZ families. The final results of the algorithms are compared in terms of cardinality, convergence, and diversity of solutions using a statistical methodology designed to indicate whether any of the criteria provides significantly better results over the whole test set. The results obtained suggest that the cone epsilon-approach is an interesting alternative for finding well-distributed fronts without the loss of efficient solutions usually presented by the epsilon-dominance.