Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems
This paper presents a pragmatic approach established on the hybridization of nature-inspired optimization algorithms and Bernstein Polynomials (BPs), achieving the optimum numeric solution for Nonlinear Optimal Control Problems (NOCPs) of dynamical systems. The approximated solution for NOCPs is obt...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10103537/ |
| _version_ | 1797840072730476544 |
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| author | Ghulam Fareed Laghari Suheel Abdullah Malik Irfan Ahmed Khan Amil Daraz Salman A. AlQahtani Hayat Ullah |
| author_facet | Ghulam Fareed Laghari Suheel Abdullah Malik Irfan Ahmed Khan Amil Daraz Salman A. AlQahtani Hayat Ullah |
| author_sort | Ghulam Fareed Laghari |
| collection | DOAJ |
| description | This paper presents a pragmatic approach established on the hybridization of nature-inspired optimization algorithms and Bernstein Polynomials (BPs), achieving the optimum numeric solution for Nonlinear Optimal Control Problems (NOCPs) of dynamical systems. The approximated solution for NOCPs is obtained by the linear combination of BPs with unknown parameters. The unknown parameters are evaluated by the conversion of NOCP to an error minimization problem and the formulation of an objective function. The Fitness Dependent Optimizer (FDO) and Genetic Algorithm (GA) are used to solve the objective function, and subsequently the optimal values of unknown parameters and the optimum solution of NOCP are attained. The efficacy of the proposed technique is assessed on three real-world NOCPs, including Van der Pol (VDP) oscillator problem, Chemical Reactor Problem (CRP), and Continuous Stirred-Tank Chemical Reactor Problem (CSTCRP). The final results and statistical outcomes suggest that the proposed technique generates a better solution and surpasses the recently represented methods in the literature, which eventually verifies the efficiency and credibility of the recommended approach. |
| first_indexed | 2024-04-09T16:08:33Z |
| format | Article |
| id | doaj.art-54cceb5e9f7b4bd3b3bb8a5fb0ac489c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T16:08:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-54cceb5e9f7b4bd3b3bb8a5fb0ac489c2023-04-24T23:00:26ZengIEEEIEEE Access2169-35362023-01-0111384853850110.1109/ACCESS.2023.326743410103537Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical SystemsGhulam Fareed Laghari0https://orcid.org/0000-0002-9084-4674Suheel Abdullah Malik1https://orcid.org/0000-0003-3797-619XIrfan Ahmed Khan2https://orcid.org/0000-0002-1872-2197Amil Daraz3https://orcid.org/0000-0002-5532-9175Salman A. AlQahtani4https://orcid.org/0000-0003-1233-1774Hayat Ullah5https://orcid.org/0000-0001-7579-2864Department of Electrical and Computer Engineering, Faculty of Engineering and Technology, International Islamic University Islamabad (IIUI), Islamabad, PakistanDepartment of Electrical and Computer Engineering, Faculty of Engineering and Technology, International Islamic University Islamabad (IIUI), Islamabad, PakistanDepartment of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaSchool of Information Science and Engineering, NingboTech University, Ningbo, ChinaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Electrical and Computer Engineering, Tandon School of Engineering, New York University, New York, NY, USAThis paper presents a pragmatic approach established on the hybridization of nature-inspired optimization algorithms and Bernstein Polynomials (BPs), achieving the optimum numeric solution for Nonlinear Optimal Control Problems (NOCPs) of dynamical systems. The approximated solution for NOCPs is obtained by the linear combination of BPs with unknown parameters. The unknown parameters are evaluated by the conversion of NOCP to an error minimization problem and the formulation of an objective function. The Fitness Dependent Optimizer (FDO) and Genetic Algorithm (GA) are used to solve the objective function, and subsequently the optimal values of unknown parameters and the optimum solution of NOCP are attained. The efficacy of the proposed technique is assessed on three real-world NOCPs, including Van der Pol (VDP) oscillator problem, Chemical Reactor Problem (CRP), and Continuous Stirred-Tank Chemical Reactor Problem (CSTCRP). The final results and statistical outcomes suggest that the proposed technique generates a better solution and surpasses the recently represented methods in the literature, which eventually verifies the efficiency and credibility of the recommended approach.https://ieeexplore.ieee.org/document/10103537/Bernstein polynomialsdynamical systemsfitness dependent optimizergenetic algorithmnonlinear optimal control problemsoptimization problem |
| spellingShingle | Ghulam Fareed Laghari Suheel Abdullah Malik Irfan Ahmed Khan Amil Daraz Salman A. AlQahtani Hayat Ullah Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems IEEE Access Bernstein polynomials dynamical systems fitness dependent optimizer genetic algorithm nonlinear optimal control problems optimization problem |
| title | Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems |
| title_full | Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems |
| title_fullStr | Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems |
| title_full_unstemmed | Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems |
| title_short | Fitness Dependent Optimizer Based Computational Technique for Solving Optimal Control Problems of Nonlinear Dynamical Systems |
| title_sort | fitness dependent optimizer based computational technique for solving optimal control problems of nonlinear dynamical systems |
| topic | Bernstein polynomials dynamical systems fitness dependent optimizer genetic algorithm nonlinear optimal control problems optimization problem |
| url | https://ieeexplore.ieee.org/document/10103537/ |
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