Scalable multi-objective optimization test problems

Abstract

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Pareto-optimal front, and ability to control difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of these features, they should be useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and having a better understanding of the working principles of MOEAs.

Keywords

ScalabilityMulti-objective optimizationComputer scienceMathematical optimizationEvolutionary algorithmSet (abstract data type)SimplicityPareto principleOptimization problemPareto optimalMachine learningMathematics

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Publication Info

Year: 2003
Type: article
Volume: 1
Pages: 825-830
Citations: 1585
Access: Closed

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APA Style

                            
                                    Kalyanmoy Deb, 
                                
                                    Lothar Thiele, 
                                
                                    Marco Laumanns
                                
                                et al.
                            
                            (2003). 
                            Scalable multi-objective optimization test problems. 
                            Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
                            , 1
                            
                            , 825-830.
                            https://doi.org/10.1109/cec.2002.1007032

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DOI: 10.1109/cec.2002.1007032

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Data completeness: 81%