Quadratic interpolation and a new local search approach to improve particle swarm optimization: Solar photovoltaic parameter estimation
The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of...
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| Vydáno v: | Expert systems with applications Ročník 236; s. 121417 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.02.2024
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters. |
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| AbstractList | The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters. |
| ArticleNumber | 121417 |
| Author | Hussein, Nazar K. Farag, M.A. Amjad, Souad Qaraad, Mohammed Mirjalili, Seyedali Elhosseini, Mostafa A. |
| Author_xml | – sequence: 1 givenname: Mohammed orcidid: 0000-0002-9513-0355 surname: Qaraad fullname: Qaraad, Mohammed email: mohammedalimohammed.qaraad@uae.ac.ma organization: TIMS, FS, Abdelmalek Essaadi University, Tetouan, Morocco – sequence: 2 givenname: Souad surname: Amjad fullname: Amjad, Souad organization: TIMS, FS, Abdelmalek Essaadi University, Tetouan, Morocco – sequence: 3 givenname: Nazar K. surname: Hussein fullname: Hussein, Nazar K. email: nazar.dikhil@tu.edu.iq organization: Department of Mathematics, College of Computer Sciences and Mathematics, Tikrit University, Iraq – sequence: 4 givenname: M.A. orcidid: 0000-0001-6322-4771 surname: Farag fullname: Farag, M.A. organization: Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt – sequence: 5 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia – sequence: 6 givenname: Mostafa A. orcidid: 0000-0002-1259-6193 surname: Elhosseini fullname: Elhosseini, Mostafa A. email: melhosseini@mans.edu.eg organization: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia |
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| Keywords | Global optimization Metaheuristic Particle swarm algorithm Photovoltaic system Swarm intelligence Hybridization Evolutionary algorithms Optimization |
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