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ICRS-Filter: A randomized direct search algorithm for constrained nonconvex optimization problems

Li, B, Nguyen, VH, Ng, CL, del Rio-Chanona, EA, Vassiliadis, VS and Arellano-Garcia, H (2016) ICRS-Filter: A randomized direct search algorithm for constrained nonconvex optimization problems Chemical Engineering Research and Design, 106. pp. 178-190.

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This work presents a novel algorithm and its implementation for the stochastic optimization of generally constrained Nonlinear Programming Problems (NLP). The basic algorithm adopted is the Iterated Control Random Search (ICRS) method of Casares and Banga (1987) with modifications such that random points are generated strictly within a bounding box defined by bounds on all variables. The ICRS algorithm serves as an initial point determination method for launching gradient-based methods that converge to the nearest local minimum. The issue of constraint handling is addressed in our work via the use of a filter based methodology, thus obviating the need for use of the penalty functions as in the basic ICRS method presented in Banga and Seider (1996), which handles only bound constrained problems. The proposed algorithm, termed ICRS-Filter, is shown to be very robust and reliable in producing very good or global solutions for most of the several case studies examined in this contribution.

Item Type: Article
Subjects : Chemical Engineering
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
Li, B
Nguyen, VH
Ng, CL
del Rio-Chanona, EA
Vassiliadis, VS
Arellano-Garcia, H
Date : 1 February 2016
DOI : 10.1016/j.cherd.2015.12.001
Copyright Disclaimer : Copyright © 2016 Institution of Chemical Engineers. All rights reserved
Uncontrolled Keywords : Science & Technology, Technology, Engineering, Chemical, Engineering, Nonconvex programming problem, Randomized search, Nonlinear programming, Stochastic search algorithms, SIMPLEX
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 24 Aug 2016 09:04
Last Modified : 31 Oct 2017 18:31

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