A Hybrid Scalarization and Adaptive epsilon-Ranking Strategy for Many-Objective Optimization


This work proposes a hybrid strategy in a two-stage search process for many-objective optimization. The first stage of the search is directed by a scalarization function and the second one by Pareto selection enhanced with Adaptive epsilon-Ranking. The scalarization strategy drives the population towards central regions of objective space, aiming to find solutions with good convergence properties to seed the second stage of the search. Adaptive epsilon-Ranking balances the search effort towards the different regions of objective space to find solutions with good convergence, spread, and distribution properties. We test the proposed hybrid strategy on MNK-Landscapes showing that performance can improve significantly on problems with more than 6 objectives.