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A two-phase variable neighborhood search for solving nonlinear optimal control problems

نویسنده:
رضا قنبری
,
Aghileh Heydari
,
Saeed Nezhadhosein
,
Reza Ghanbari
سال
: 2015
چکیده: In this paper, a two-phase algorithm, namely IVNS, is proposed for solving nonlinear optimal control problems. In each phase of the algorithm, we use a variable neighborhood search (VNS), which performs a uniform distribution in the shaking step and the successive quadratic programming, as the local search step. In the first phase, VNS starts with a completely random initial solution of control input values. To increase the accuracy of the solution obtained from the phase 1, some new time nodes are added and the values of the new control inputs are estimated by spline interpolation. Next, in the second phase, VNS restarts by the solution constructed by the phase 1. The proposed algorithm is implemented on more than 20 well-known benchmarks and real world problems, then the results are compared with

some recently proposed algorithms. The numerical results show that IVNS can find the best solution on 84% of test problems. Also, to compare the IVNS with a common VNS (when the number of time nodes is same in both phases), a computational study is done. This study shows that IVNS needs

less computational time with respect to common VNS, when the quality of solutions are not difierent significantly.
یو آر آی: http://libsearch.um.ac.ir:80/fum/handle/fum/3353446
کلیدواژه(گان): Nonlinear optimal control problem,Variable neighborhood search,Successive quadratic programming
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    A two-phase variable neighborhood search for solving nonlinear optimal control problems

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contributor authorرضا قنبریen
contributor authorAghileh Heydarien
contributor authorSaeed Nezhadhoseinen
contributor authorReza Ghanbarifa
date accessioned2020-06-06T13:24:13Z
date available2020-06-06T13:24:13Z
date issued2015
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/3353446
description abstractIn this paper, a two-phase algorithm, namely IVNS, is proposed for solving nonlinear optimal control problems. In each phase of the algorithm, we use a variable neighborhood search (VNS), which performs a uniform distribution in the shaking step and the successive quadratic programming, as the local search step. In the first phase, VNS starts with a completely random initial solution of control input values. To increase the accuracy of the solution obtained from the phase 1, some new time nodes are added and the values of the new control inputs are estimated by spline interpolation. Next, in the second phase, VNS restarts by the solution constructed by the phase 1. The proposed algorithm is implemented on more than 20 well-known benchmarks and real world problems, then the results are compared with

some recently proposed algorithms. The numerical results show that IVNS can find the best solution on 84% of test problems. Also, to compare the IVNS with a common VNS (when the number of time nodes is same in both phases), a computational study is done. This study shows that IVNS needs

less computational time with respect to common VNS, when the quality of solutions are not difierent significantly.
en
languageEnglish
titleA two-phase variable neighborhood search for solving nonlinear optimal control problemsen
typeJournal Paper
contenttypeExternal Fulltext
subject keywordsNonlinear optimal control problemen
subject keywordsVariable neighborhood searchen
subject keywordsSuccessive quadratic programmingen
journal titleIranian Journal of Numerical Analysis and Optimizationfa
pages13-36
journal volume5
journal issue1
identifier linkhttps://profdoc.um.ac.ir/paper-abstract-1048041.html
identifier articleid1048041
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