A Dual Recurrent Neural Network-based Hybrid Approach for Solving Convex Quadratic Bi-Level Programming Problem

•A novel NN-based hybrid method for solving quadratic-BLPPs is presented.•Proposed algorithm combines GA (handles upper-level problem) and DRNN (for lower-level decision problem).•GA solves the upper-level decision problem by choosing candidate solutions and passing them to the lower-level.•Paramete...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 407; S. 136 - 154
Hauptverfasser: WATADA, Junzo, ROY, Arunava, LI, Jingru, WANG, Bo, WANG, Shuming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 24.09.2020
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ISSN:0925-2312, 1872-8286
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Abstract •A novel NN-based hybrid method for solving quadratic-BLPPs is presented.•Proposed algorithm combines GA (handles upper-level problem) and DRNN (for lower-level decision problem).•GA solves the upper-level decision problem by choosing candidate solutions and passing them to the lower-level.•Parameterized dual-NN is used in the lower-level problem to determine the optimal solutions.•This combination offers many benefits like parallel computing, faster convergence to global optimum, etc. The current paper presents a neural network-based hybrid strategy that combines a Genetic Algorithm (GA) and a Dual Recurrent Neural Network (DRNN) for efficiently and accurately solving the quadratic-Bi-level Programming Problem (BLPP). In this model, the GA is used to handle the upper-level decision problem by choosing desirable solution candidates and passing them to the lower-level problem. Subsequently, in the lower-level, the parameterized-DRNN is used to determine possible optimal solutions. This combination offers several benefits such as being a parallel computing structure, the RNN offers faster convergence to the optimum for the lower-level decision problem and it also helps to quickly and accurately determining the global optimal. Moreover, the GA can quickly reach the global optima and can search without becoming stuck to the local optimal. Additionally, by choosing desirable initialization of parameters, the proposed algorithm reaches the optimum with higher accuracy. Apart from that, there are still a few utilizations of hybrid NN-based methods for solving BLPPs. Hence, we believe the proposed algorithm will contribute to solving quadratic-BLPPs involved in various engineering, management, and finance applications. The accuracy and efficiency of the proposed method have been found better than the existing and widely used approaches, while doing experimental verification using four well-known examples used in prior works.
AbstractList •A novel NN-based hybrid method for solving quadratic-BLPPs is presented.•Proposed algorithm combines GA (handles upper-level problem) and DRNN (for lower-level decision problem).•GA solves the upper-level decision problem by choosing candidate solutions and passing them to the lower-level.•Parameterized dual-NN is used in the lower-level problem to determine the optimal solutions.•This combination offers many benefits like parallel computing, faster convergence to global optimum, etc. The current paper presents a neural network-based hybrid strategy that combines a Genetic Algorithm (GA) and a Dual Recurrent Neural Network (DRNN) for efficiently and accurately solving the quadratic-Bi-level Programming Problem (BLPP). In this model, the GA is used to handle the upper-level decision problem by choosing desirable solution candidates and passing them to the lower-level problem. Subsequently, in the lower-level, the parameterized-DRNN is used to determine possible optimal solutions. This combination offers several benefits such as being a parallel computing structure, the RNN offers faster convergence to the optimum for the lower-level decision problem and it also helps to quickly and accurately determining the global optimal. Moreover, the GA can quickly reach the global optima and can search without becoming stuck to the local optimal. Additionally, by choosing desirable initialization of parameters, the proposed algorithm reaches the optimum with higher accuracy. Apart from that, there are still a few utilizations of hybrid NN-based methods for solving BLPPs. Hence, we believe the proposed algorithm will contribute to solving quadratic-BLPPs involved in various engineering, management, and finance applications. The accuracy and efficiency of the proposed method have been found better than the existing and widely used approaches, while doing experimental verification using four well-known examples used in prior works.
Author LI, Jingru
WATADA, Junzo
WANG, Bo
WANG, Shuming
ROY, Arunava
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Keywords Recurrent Neural Networks (RNN)
Genetic Algorithm (GA)
Hybrid Strategy
Quadratic Bi-Level Programming Problem (BLPP)
Language English
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Snippet •A novel NN-based hybrid method for solving quadratic-BLPPs is presented.•Proposed algorithm combines GA (handles upper-level problem) and DRNN (for...
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SubjectTerms Genetic Algorithm (GA)
Hybrid Strategy
Quadratic Bi-Level Programming Problem (BLPP)
Recurrent Neural Networks (RNN)
Title A Dual Recurrent Neural Network-based Hybrid Approach for Solving Convex Quadratic Bi-Level Programming Problem
URI https://dx.doi.org/10.1016/j.neucom.2020.04.013
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