Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems


The averaged Hausdorff distance Delta(p) 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 Delta(p)-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 Delta(p). In this work a conceptual generalization of the Delta(p)-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.