Please use this identifier to cite or link to this item: https://doi.org/10.1137/120864738
Title: A derivative-free trust-region method for biobjective optimization
Authors: Ryu, J.-H.
Kim, S. 
Keywords: Biobjective optimization
Derivative-free algorithm
Multiobjective optimization
Pareto dominance
Trust-region method
Issue Date: 2014
Source: Ryu, J.-H., Kim, S. (2014). A derivative-free trust-region method for biobjective optimization. SIAM Journal on Optimization 24 (1) : 334-362. ScholarBank@NUS Repository. https://doi.org/10.1137/120864738
Abstract: We consider unconstrained black-box biobjective optimization problems in which analytic forms of the objective functions are not available and function values can be obtained only through computationally expensive simulations. We propose a new algorithm to approximate the Pareto optimal solutions of such problems based on a trust-region approach. At every iteration, we identify a trust region, then sample and evaluate points from it. To determine nondominated solutions in the trust region, we employ a scalarization method to convert the two objective functions into one. We construct and optimize quadratic regression models for the two original objectives and the converted single objective. We then remove dominated points from the current Pareto approximation and construct a new trust region around the most isolated point in order to explore areas that have not been visited. We prove convergence of the method under general regularity conditions and present numerical results suggesting that the method efficiently generates well-distributed Pareto optimal solutions. © 2014 Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/86988
ISSN: 10526234
DOI: 10.1137/120864738
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