Multiobjective optimization using an adaptive weighting scheme. (2nd January 2016)

• Record Type: Journal Article
• Authors: Deshpande, Shubhangi; Watson, Layne T.; Canfield, Robert A.
• Is Part Of: Optimization methods and software. Volume 31:Number 1(2016)
• Page Start: 110
• Page End: 133
• Publication Date: 2016-01-02
• Subjects: multiple objective programming -- DIRECT -- MADS -- surrogates -- triangulation; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2015.1048861
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : A new Pareto front approximation method is proposed for multiobjective optimization problems (MOPs) with bound constraints. The method employs a hybrid optimization approach using two derivative-free direct search techniques, and intends to solve black box simulation-based MOPs where the analytical form of the objectives is not known and/or the evaluation of the objective function(s) is very expensive. A new adaptive weighting scheme is proposed to convert a multiobjective optimization problem to a single objective optimization problem. Another contribution of this paper is the generalization of the star discrepancy-based performance measure for problems with more than two objectives. The method is evaluated using five test problems from the literature, and a realistic engineering problem. Results show that the method achieves an arbitrarily close approximation to the Pareto front with a good collection of well-distributed nondominated points for all six test problems.