A hybrid scalarization and adaptive ε-ranking strategy for many-objective optimization

Authors:
Hernán Aguirre;Kiyoshi Tanaka
Affiliations:
International Young Researcher Empowerment Center, Nagano, Japan and Faculty of Engineering, Shinshu University, Nagano, Japan;Faculty of Engineering, Shinshu University, Nagano, Japan
Venue:
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Year:
2010

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Abstract

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 ε-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 ε-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.