A Bounded Archiver for Hausdorff Approximations of the Pareto Front for Multi-Objective Evolutionary Algorithms

Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for the numerical treatment of multi-objective optimization problems (MOP) during last three decades. One important task within MOEAs is archiving (or selection) computed candidate solutions, since one can expect that an MOP has infinitely many solutions. We present and analyze in this work ArchiveUpdateHD, which a bounded archiver aims Hausdorff approximations Pareto front. show sequence archives generated by ArchiveUpdateHD yields under certain (mild) assumptions with probability after finitely steps Δ+-approximation front, where value Δ+ run algorithm without any prior knowledge The great importance decision maker, it measure “completeness” front approximation. Numerical results on several well-known academic test as well usage external state-of-the-art indicate benefit novel strategy.