Self-Organizing Maps
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Active Learning with Adaptive Grids
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective optimization on a budget of 250 evaluations
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Self-adaptation techniques applied to multi-objective evolutionary algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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This work introduces a new recombination and a new mutation operator for an accelerated evolutionary algorithm in the context of Pareto optimization. Both operators are based on a self-organizing map, which is actively learning from the evolution in order to adapt the mutation step size and improve convergence speed. Standard selection operators can be used in conjunction with these operators.The new operators are applied to the Pareto optimization of an airfoil for minimizing the aerodynamic profile losses at the design operating point and maximizing the operating range. The profile performance is analyzed with a quasi 3D computational fluid dynamics (Q3D CFD) solver for the design condition and two off-design conditions (one positive and one negative incidence).The new concept is to define a free scaling factor, which is multiplied to the off-design incidences. The scaling factor is considered as an additional design variable and at the same time as objective function for indexing the operating range, which has to be maximized. We show that 2 off-design incidences are sufficient for the Pareto optimization and that the computation of a complete loss polar is not necessary. In addition, this approach answers the question of how to set the incidence values by defining them as design variables of the optimization.