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
Hlavní autoři: Qaraad, Mohammed, Amjad, Souad, Hussein, Nazar K., Farag, M.A., Mirjalili, Seyedali, Elhosseini, Mostafa A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2024
Témata:
Evolutionary algorithms
Global optimization
Hybridization
Metaheuristic
Optimization
Particle swarm algorithm
Photovoltaic system
Swarm intelligence
Global optimization
Metaheuristic
Particle swarm algorithm
Photovoltaic system
Swarm intelligence
Hybridization
Evolutionary algorithms
Optimization
ISSN:0957-4174, 1873-6793
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Shrnutí: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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.121417