| Abstract: |
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multi-objective optimization problems, where the goal is to find a number of
Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Pareto-optimal set with a widely spread
distribution of solutions. However, none of the multi-objective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-optimal solutions with a wide diversity among the
solutions. In this paper we discuss why a number of earlier MOEAs do not have such properties. Based on the concept of epsdom, new archiving strategies are proposed that overcome this fundamental
problem and provably lead to MOEAs which have both the desired convergence and distribution properties. A number of modifications to the baseline algorithm are also suggested. The concept of
epsdom introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike. |
