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A fast and elitist multiobjective genetic algorithm: NSGA-II
This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Expand
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front. Expand
Constrained Test Problems for Multi-objective Evolutionary Optimization
A number of test problems used in the literature are reviewed and a set of tunable test problems for constraint handling are suggested which can evaluate the constraint handling MOEAs well. Expand
Multiobjectivization with NSGA-II on the Noiseless BBOB Testbed
The idea of multiobjectivization is to reformulate a singleobjective problem as a multiobjective one. In one of the scarce studies proposing this idea for problems in continuous domains, the distanceExpand