Comparison of Multiobjective Evolutionary Algorithms on Test Functions of Different Difficulty

Evolutionary algorithms EAs have become es tablished as the method at hand to explore the Pareto optimal front in multiobjective optimiza tion problems This is not only because there are hardly any alternatives in the eld of multiob jective optimization due to their inherent paral lelism and their capability to exploit similarities of solutions by crossover they are able to capture several Pareto optimal solutions in a single opti mization run The numerous applications and the rapidly growing interest in the area of multiobjec tive EAs take this fact into account