A combined scalarization method for multi-objective optimization problems
Yuan-mei Xia, Xin-min Yang, Kequan Zhao
Abstract
<p style='text-indent:20px;'>In this paper, we propose a new combined scalarization method of multi-objective optimization problems by using the surplus variables and the generalized Tchebycheff norm and then use it to obtain some equivalent scalarization characterizations of (weakly, strictly, properly) efficient solutions by adjusting the range of parameters. These scalarization results do not need any convexity assumption conditions of objective functions. Furthermore, we establish some scalarization results of approximate solutions by means of the method. Moreover, we also present some examples to illustrate the main results.
Topics & Concepts
ConvexityMathematical optimizationRange (aeronautics)Norm (philosophy)Multi-objective optimizationComputer scienceMathematicsApplied mathematicsLawComposite materialFinancial economicsEconomicsPolitical scienceMaterials scienceOptimization and Variational AnalysisAdvanced Optimization Algorithms ResearchAdvanced Multi-Objective Optimization Algorithms