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.
|Subjects :||Chemical Engineering|
|Divisions :||Faculty of Engineering and Physical Sciences > Chemical and Process Engineering|
|Date :||1 February 2016|
|Identification Number :||https://doi.org/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 :||24 Aug 2016 09:04|
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