Handling many-objective problems using an improved NSGA-II procedure

Kalyanmoy Deb; Himanshu Jain

doi:10.1109/cec.2012.6256519

Abstract

Handling many-objective problems is one of the primary concerns to EMO researchers. In this paper, we discuss a number of viable directions for developing a potential EMO algorithm for many-objective optimization problems. Thereafter, we suggest a reference-point based many-objective NSGA-II (or MO-NSGA-II) that emphasizes population members which are non-dominated yet close to a set of well-distributed reference points. The proposed MO-NSGA-II is applied to a number of many-objective test problems having three to 10 objectives (constrained and unconstrained) and compared with a recently suggested EMO algorithm (MOEA/D). The results reveal difficulties of MOEA/D in solving large-sized and differently-scaled problems, whereas MO-NSGA-II is reported to show a desirable performance on all test-problems used in this study. Further investigations are needed to test MO-NSGA-II's full potential.

Keywords

Mathematical optimizationMulti-objective optimizationSet (abstract data type)Computer sciencePopulationPoint (geometry)Test (biology)Optimization problemEvolutionary algorithmMathematicsMedicine

Affiliated Institutions

Indian Institute of Technology Kanpur IN

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Publication Info

Year: 2012
Type: article
Pages: 1-8
Citations: 97
Access: Closed

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APA Style

                            
                                    Kalyanmoy Deb, 
                                
                                    Himanshu Jain
                                
                            (2012). 
                            Handling many-objective problems using an improved NSGA-II procedure. 
                            
                            , 1-8.
                            https://doi.org/10.1109/cec.2012.6256519

Identifiers

DOI: 10.1109/cec.2012.6256519