An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming Problems
In the current work, we devised a hybrid method involving a Double-Layer Neural Network (DLNN) for solving a quadratic Bi-Level Programming Problem (BLPP). For an efficient and effective solution of such problems, the proposed potential methodology includes an improved Artificial Bee Colony (ABC) al...
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IEEE
2020-01-01
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Online Access: | https://ieeexplore.ieee.org/document/8963727/ |
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author | Junzo Watada Arunava Roy Bo Wang Shing Chiang Tan Bing Xu |
author_facet | Junzo Watada Arunava Roy Bo Wang Shing Chiang Tan Bing Xu |
author_sort | Junzo Watada |
collection | DOAJ |
description | In the current work, we devised a hybrid method involving a Double-Layer Neural Network (DLNN) for solving a quadratic Bi-Level Programming Problem (BLPP). For an efficient and effective solution of such problems, the proposed potential methodology includes an improved Artificial Bee Colony (ABC) algorithm, a Hopfield Network (HN), and a Boltzmann Machine (BM). The improved ABC algorithm accommodates upper-level decision problems by selecting a set of potential solutions from all combinations of solutions. However, for lower-level decision problem, HN and BM are amalgamated to manifest a DLNN that initially generates its structure by choosing a limited number of units, and will subsequently converge to an optimal solution/unit among those units and hence, constitutes an effective, efficient solution technique.We compared the accuracy, computational time and effectiveness (ability to find the true optimum) of the proposed DLNN with improved-ABC, DLNN with PSO (where PSO replaces the improved-ABC in the upper-level problem of the proposed DLNN with improved-ABC), DLNN with GA (where GAreplaces the improved-ABC in the upper-level of the proposed algorithm) and other conventional approaches and found the proposed DLNN with improved-ABC can yield high quality global optimal solutions with higher accuracy in relatively smaller time. |
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issn | 2169-3536 |
language | English |
last_indexed | 2024-12-19T07:58:34Z |
publishDate | 2020-01-01 |
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spelling | doaj.art-18b0f327b2a74b2aab88e1c85075db0d2022-12-21T20:29:55ZengIEEEIEEE Access2169-35362020-01-018215492156410.1109/ACCESS.2020.29677878963727An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming ProblemsJunzo Watada0https://orcid.org/0000-0002-3322-2086Arunava Roy1https://orcid.org/0000-0003-3523-1960Bo Wang2https://orcid.org/0000-0002-9264-9741Shing Chiang Tan3https://orcid.org/0000-0002-1267-1894Bing Xu4https://orcid.org/0000-0002-6545-1385Research Institute of Quantitative Economics, Zhejiang Gongshang University, Hangzhou, ChinaDepartment of Computer and Information Sciences, Universiti Technologi Petronas (UTP), Seri Iskandar, MalaysiaManagement and Engineering Department, Nanjing University, Nanjing, ChinaFaculty of Information Science and Technology, Multimedia University, Cyberjaya, MalaysiaResearch Institute of Quantitative Economics, Zhejiang Gongshang University, Hangzhou, ChinaIn the current work, we devised a hybrid method involving a Double-Layer Neural Network (DLNN) for solving a quadratic Bi-Level Programming Problem (BLPP). For an efficient and effective solution of such problems, the proposed potential methodology includes an improved Artificial Bee Colony (ABC) algorithm, a Hopfield Network (HN), and a Boltzmann Machine (BM). The improved ABC algorithm accommodates upper-level decision problems by selecting a set of potential solutions from all combinations of solutions. However, for lower-level decision problem, HN and BM are amalgamated to manifest a DLNN that initially generates its structure by choosing a limited number of units, and will subsequently converge to an optimal solution/unit among those units and hence, constitutes an effective, efficient solution technique.We compared the accuracy, computational time and effectiveness (ability to find the true optimum) of the proposed DLNN with improved-ABC, DLNN with PSO (where PSO replaces the improved-ABC in the upper-level problem of the proposed DLNN with improved-ABC), DLNN with GA (where GAreplaces the improved-ABC in the upper-level of the proposed algorithm) and other conventional approaches and found the proposed DLNN with improved-ABC can yield high quality global optimal solutions with higher accuracy in relatively smaller time.https://ieeexplore.ieee.org/document/8963727/Quadratic-BLPPHopfield networkBoltzmann machinedouble-layered neural networkartificial bee colony algorithm |
spellingShingle | Junzo Watada Arunava Roy Bo Wang Shing Chiang Tan Bing Xu An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming Problems IEEE Access Quadratic-BLPP Hopfield network Boltzmann machine double-layered neural network artificial bee colony algorithm |
title | An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming Problems |
title_full | An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming Problems |
title_fullStr | An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming Problems |
title_full_unstemmed | An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming Problems |
title_short | An Artificial Bee Colony-Based Double Layered Neural Network Approach for Solving Quadratic Bi-Level Programming Problems |
title_sort | artificial bee colony based double layered neural network approach for solving quadratic bi level programming problems |
topic | Quadratic-BLPP Hopfield network Boltzmann machine double-layered neural network artificial bee colony algorithm |
url | https://ieeexplore.ieee.org/document/8963727/ |
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