Finding evenly spaced pareto fronts for three-objective optimization problems

Abstract

The averaged Hausdorff distance Δp is a performance indicator in multi-objective evolutionary optimization which simultaneously takes into account proximity to the true Pareto front and uniform spread of solutions. Recently, the multi-objective evolutionary algorithm Δp -EMOA was introduced which successfully generates evenly spaced Pareto front approximations for bi-objective problems by integrating an external archiving strategy into the SMS-EMOA based on Δp . In this work a conceptual generalization of the Δp -EMOA for higher objective space dimensions is presented and experimentally compared to state-of-the art EMOA as well as specialized EMOA variants on three-dimensional optimization problems. © Springer-Verlag Berlin Heidelberg 2013.