A conic scalarization method in multi‐objective optimization

A conic scalarization method in multi‐objective optimization

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Article ID: iaor20134034
Volume: 56
Issue: 2
Start Page Number: 279
End Page Number: 297
Publication Date: Jun 2013
Journal: Journal of Global Optimization
Authors:
Keywords: programming: nonlinear
Abstract:

This paper presents the conic scalarization method for scalarization of nonlinear multi‐objective optimization problems. We introduce a special class of monotonically increasing sublinear scalarizing functions and show that the zero sublevel set of every function from this class is a convex closed and pointed cone which contains the negative ordering cone. We introduce the notion of a separable cone and show that two closed cones (one of them is separable) having only the vertex in common can be separated by a zero sublevel set of some function from this class. It is shown that the scalar optimization problem constructed by using these functions, enables to characterize the complete set of efficient and properly efficient solutions of multi‐objective problems without convexity and boundedness conditions. By choosing a suitable scalarizing parameter set consisting of a weighting vector, an augmentation parameter, and a reference point, decision maker may guarantee a most preferred efficient or properly efficient solution.

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