Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
A hybrid evolutionary multi-objective and SQP based procedure for constrained optimization
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Statistical methods for convergence detection of multi-objective evolutionary algorithms
Evolutionary Computation
A taxonomy of online stopping criteria for multi-objective evolutionary algorithms
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Leveraging indicator-based ensemble selection in evolutionary multiobjective optimization algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Industry applications of multiobjective optimization problems mostly are characterized by the demand for high quality solutions on the one hand. On the other hand an optimization result is desired which at any rate meets the time constraints for the evolutionary multiobjective algorithms (EMOA). The handling of this trade-off is a frequently discussed issue in multiobjective evolutionary optimization. Recently an online convergence detection algorithm (OCD) for EMOA based on statistical testing has been introduced. OCD is independent from any knowledge of the true Pareto front of the optimization problem. It automatically stops at the EMOA generation in which either only a very small variation or a trend stagnation of a set of multiobjective performance indicators are detected for a predefined number of generations. In the course of the paper, OCD is applied to two aerodynamic test cases provided by a global player of the aircraft industry. It is shown that OCD performs extremely well on these problems in terms of saved function evaluations and EMOA performance after the OCD stop generation.