Yazar "Abdel-Salam, Mahmoud" seçeneğine göre listele

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    Design optimization and real-time implementation of an LSPMSM for efficiency enhancement
    (Nature Portfolio, 2025) Ocak, Cemil; Yenipinar, Burak; Celik, Emre; Abdel-Salam, Mahmoud; Tejani, Ghanshyam G.; Mousavirad, Seyed Jalaleddin
    This study presents the design optimization and experimental validation of a Line-Start Permanent Magnet Synchronous Motor (LSPMSM) aimed at achieving IE4 efficiency class. An IE1 class induction motor (IM) was used as a reference. Only the rotor structure was modified, while the stator geometry, winding, and mechanical components were kept unchanged. The optimization process focused on rotor slot geometry, magnet placement, magnet dimensions, and core length, employing a Multi-Objective Genetic Algorithm (MOGA) to maximize efficiency while maintaining cost-effectiveness. Following the optimization, six candidate designs were evaluated based on demagnetization prediction, synchronization performance, and starting torque capability. Among them, Design C demonstrated the highest overall performance. Finite Element Analysis (FEA) confirmed that Design C met IE4 efficiency standards with a calculated efficiency of 92.15%. This result was later experimentally verified at 91.95% through thermal testing. The study further examined the cost and payback period scenarios for adopting LSPMSMs in industrial applications. Three implementation strategies were analyzed: replacing only the rotor, purchasing a new IE4 LSPMSM instead of an IE1 motor, and replacing an operational IE1 motor with an IE4 LSPMSM. The results indicated that efficiency improvement could be achieved with minimal modifications. The payback period varied depending on the investment strategy. The findings demonstrate that high-efficiency LSPMSMs can serve as direct replacements for induction motors, offering energy savings and improved performance while maintaining compatibility with existing motor housings and components.
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    Elite Bernoulli-based mutated dung beetle algorithm for global complex problems and parameter estimation of solar photovoltaic models
    (Nature Portfolio, 2025) Elhosseny, Mohamed; Abdel-Salam, Mahmoud; Nayyar, Anand; Celik, Emre; Mahajan, Shubham; Bacanin, Nebojsa; El-Hasnony, Ibrahim M.
    The Dung Beetle Optimization (DBO) algorithm is a relatively recent metaheuristic known for its simplicity, versatility, and low parameter dependence, making it a valuable tool for solving complex optimization problems. Despite its potential, DBO suffers from limitations such as slow convergence and premature stagnation in local optima. To address these critical issues, this paper introduces a novel enhanced variant named Elite Bernoulli-based Mutated Dung Beetle Optimizer with Local Escaping Operator (EBMLO-DBO), specifically designed to improve the convergence speed, search capability, and robustness of the original DBO algorithm. The motivation for this enhancement stems from DBO's limited performance in high-dimensional and non-convex problems, where it often fails to maintain an effective balance between exploration and exploitation. The novelty of the proposed EBMLO-DBO lies in the integration of four key strategies tailored to overcome these weaknesses: (i) Bernoulli map-based initialization to enhance population diversity and ensure a better global search foundation; (ii) Morlet Wavelet mutation to introduce adaptive local refinements and help the algorithm escape local optima; (iii) elite guidance to accelerate convergence by directing the population toward high-quality regions; and (iv) a local escaping operator (LEO) to dynamically refine the search process and strengthen exploitation without sacrificing exploration. The performance of EBMLO-DBO is rigorously validated using the CEC2017 and CEC2022 benchmark suites, where it achieves Friedman ranks of 1.83 and 2.7 respectively, consistently surpassing eleven state-of-the-art algorithms including PSO, HHO, WOA, and advanced methods like CMAES and IMODE. In benchmark function optimization, EBMLO-DBO demonstrates superior performance by achieving first rank in 50% of CEC2022 functions and obtaining the lowest average fitness values in 18 out of 29 CEC2017 functions. For photovoltaic parameter estimation applications, EBMLO-DBO exhibits exceptional accuracy with RMSE values of 9.8602E-4 for single diode models, 9.81307E-4 for double diode models, and 2.32066E-3 for PV module models, achieving top performance ranks of 1.45, 1.42, and 1.74, respectively. Statistical analysis using Wilcoxon signed-rank test at significance level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =0.05$$\end{document} confirms the significant superiority of EBMLO-DBO over all compared algorithms, thereby validating the effectiveness and reliability of the proposed enhancements. Overall, the results state that EBMLO-DBO offers a significantly improved search performance and solution quality compared to the original DBO and related methods, thereby justifying the necessity and effectiveness of the proposed enhancements.
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    Novel distance-fitness learning scheme for ameliorating metaheuristic optimization
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2025) Celik, Emre; Houssein, Essam H.; Abdel-Salam, Mahmoud; Oliva, Diego; Tejani, Ghanshyam G.; Ozturk, Nihat; Sharma, Sunil Kumar
    An important portion of metaheuristic algorithms is guided by the fittest solution obtained so far. Searching around the fittest solution is beneficial for speeding up convergence, but it is detrimental considering local minima stagnation and premature convergence. A novel distance-fitness learning (DFL) scheme that provides better searchability and greater diversity is proposed to resolve these. The method allows search agents in the population to actively learn from the fittest solution, the worst solution, and an optimum distance-fitness (ODF) candidate. This way, it aims at approaching both the fittest solution and ODF candidate while at the same time moving away from the worst solution. The effectiveness of our proposal is evaluated by integrating it with the reptile search algorithm (RSA), which is an interesting algorithm that is simple to code but suffers from stagnating in local minima, converging too early, and a lack of sufficient global searchability. Empirical results from solving 23 standard benchmark functions, 10 Congresses on Evolutionary Computation (CEC) 2020 test functions, and 2 real-world engineering problems reveal that DFL boosts the capability of RSA significantly. Further, the comparison of DFL-RSA with popular algorithms vividly signifies the potential and superiority of the method over most of the problems in terms of solution precision.
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    Reconfigured single- and double-diode models for improved modelling of solar cells/modules
    (Nature Portfolio, 2025) Celik, Emre; Karayel, Mehmet; Maden, Dincer; Abdel-Salam, Mahmoud; Ozturk, Nihat; Kaplan, Orhan; Tejani, Ghanshyam G.
    Proper modeling of PV cells/modules through parameter identification based on the real current-voltage (I-V) data is important for the efficiency of PV systems. Most related works have concentrated on the classical single-diode model (SDM) and double-diode model (DDM) and their parameter extraction by various metaheuristic algorithms. In order to render more accurate and representative modeling, this paper adds a small resistance in series with the diodes in SDM and DDM. The new models are named reconfigured SDM (Reconfig-SDM) and reconfigured DDM (Reconfig-DDM), and they have not been studied so far as we know. A squirrel search algorithm (SSA) is employed to globally find the parameters of the new models. The performance achieved is experimentally tested on both a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module located at D & uuml;zce University in T & uuml;rkiye. A vivid comparison of experimental findings, observation, and analysis clearly demonstrates that the proposed Reconfig-SDM and Reconfig-DDM tuned by the SSA have better capacity and effectiveness for modeling PV devices than some cutting-edge approaches. Specifically, compared with the best-performing approach in the literature, Reconfig-SDM and Reconfig-DDM could reduce the error rate up to 0.37% and 2.58% for the solar cell, and 3.21% and 29.0% for the solar module.