Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading by Ouatman Hamid, Boutammachte Nour-Eddine

“…This study conducts a comparative analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for Flexible Power Point Tracking (FPPT) in photovoltaic (PV) systems. …”
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A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions by Boyan Huang, Kai Song, Shulin Jiang, Zhenqing Zhao, Zhiqiang Zhang, Cong Li, Jiawen Sun

“…Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. …”
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Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies by Denesh Sooriamoorthy, Aaruththiran Manoharan, Siva Kumar Sivanesan, Soon Kian Lun, Alexander Chee Hon Cheong, Sathish Kumar Selva Perumal

“…Metaheuristic algorithms such as Particle Swarm Optimization (PSO) are explored extensively for MPPT applications. …”
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Fault localization for automatic train operation based on the adaptive error locating array algorithm by Yanpeng Zhang, Yuxiang Cao

“…These cases are designed to locate the MFS more easily in the given parameter range using the Adaptive Particle Swarm Optimization (APSO) algorithm. …”
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Photovoltaic energy harvesting booster under partially shaded conditions using MPPT based sand cat swarm optimizer by Moch Rafi Damas Abdilla, Novie Ayub Windarko, Bambang Sumantri

“…The suggested SCSO performance is evaluated under a variety of weather situations, including both instances of partially shaded and uniform irradiance. The SCSO results are juxtaposed with other existing bio-inspired algorithms, such as grey wolf optimization (GWO), particle swarm optimization (PSO), and tunicate swarm algorithm (TSA). …”
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An efficient metaheuristic optimization algorithm for optimal power extraction from PV systems under various weather and load-changing conditions by Md.Al Imran Fahim, Md.Salah Uddin Yusuf, Monira Islam, Munshi Jawad Ibne Azad

“…A comprehensive study compares the HHO technique with established methods such as perturb and observe (P&O), modified P&O (MP&O), incremental conductance (IC), Spline MPPT, particle swarm optimization (PSO), grasshopper optimization (GHO), and grey wolf optimization (GWO) across fast-changing irradiance, partial shading, complex partial shading, and load-changing conditions. …”
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A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells by Bhukya Laxman, Ramesh Gugulothu, Surender Reddy Salkuti

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Comparative Study of White Shark Optimization and Combined Meta-Heuristic Algorithm for Enhanced MPPT in Photovoltaic Systems by Fajar Kurnia Al Farisi, Zhi-Kai Fan, Kuo-Lung Lian

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A novel global MPPT method based on sooty tern optimization for photovoltaic systems under complex partial shading by Mohammed Taha Kaaitan, Rashid Ali Fayadh, Zuhair S. AL-sagar, Salam J. Yaqoob, Mohit Bajaj, Mebratu Sintie Geremew

“…Performance evaluation against benchmark algorithms—Perturb & Observe (P&O), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—revealed that STOA consistently outperformed its counterparts. …”
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Classification of biomedical lung cancer images using optimized binary bat technique by constructing oblique decision trees by Shobha Aswal, Neelu Jyothi Ahuja, Ritika Mehra

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Quantum Snowflake Algorithm (QSA): A Snowflake-Inspired, Quantum-Driven Metaheuristic for Large-Scale Continuous and Discrete Optimization with Application to the Traveling Salesma... by Zeki Oralhan, Burcu Oralhan

“…The Quantum Snowflake Algorithm (QSA) is a novel metaheuristic for both continuous and discrete optimization problems, combining collision-based diversity, quantum-inspired tunneling, superposition-based partial solution sharing, and local refinement steps. …”
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Adaptive Control Parameter Optimization of Permanent Magnet Synchronous Motors Based on Super-Helical Sliding Mode Control by Lingtao Kong, Hongxin Zhang, Tiezhu Zhang, Junyi Wang, Chaohui Yang, Zhen Zhang

“…The Non-dominated Sorting Genetic Algorithm II and Multi-objective Particle Swarm Optimization are employed to effectively optimize control parameters, thereby mitigating motor torque and speed overshoot. …”
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An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation by Rui Yang, Yongbao Liu, Xing He, Zhimeng Liu

“…When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. …”
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A Novel Grey Prediction Model: A Hybrid Approach Based on Extension of the Fractional Order Discrete Grey Power Model with the Polynomial-Driven and PSO-GWO Algorithm by Baohua Yang, Xiangyu Zeng, Jinshuai Zhao

“…The estimation of unknown parameters is carried out by leveraging a hybrid optimization algorithm, which integrates Particle Swarm Optimization (PSO) and the Grey Wolf Optimization (GWO) algorithm. …”
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K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems by Djamel Guessoum, Maen Takrouri, Mohammad Rabih, Maissa Farhat, Sufian A. Badawi

“…The proposed method has surpassed various optimization techniques for MPPT, including particle swarm optimization (PSO) and Cuckoo search (CS). …”
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Hardware-in-loop implementation of an adaptive MPPT controlled PV-assisted EV charging system with vehicle-to-grid integration by Surabhi Singh, Hari Om Bansal

“…This paper developed and compared perturb and observe (P&O), Particle swarm optimization (PSO), and hybrid PSO + Adaptive neuro-fuzzy inference system (ANFIS) based algorithm for MPPT. …”
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Predicting the Traffic Crashes of Taxi Drivers by Applying the Non-Linear Learning of ANFIS-PSO with M5 Model Tree by E. Abbasi, M. Hadji Hosseinlou

“…A novel technique is been developed by applying nonlinear-learning of composition model of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization (PSO) with M5 model tree. …”
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Water quality index modelling and its application on artificial intelligence (AI) in conjunction with machine learning (ML) methodologies for mapping surface water potential zones... by Abhijeet Das

“…To evaluate the contamination level, a basic standard reference i.e., World Health Organization guidelines is implanted to decipher the values ranging from natural to anthropogenic contribution.In the Mahanadi River Basin, Odisha, however, this study has highlighted the evaluation of surface water quality (WQ) for drinking reasons by the combined use of Machine Learning (ML) methodologies like Genetic Algorithm Particle Swarm Optimization-based WQI (GAPSO-WQI), with dependability-oriented decision-making approaches such as Firefly Algorithm (FA) and Algorithm of Weeds (AW), that have been used for river water quality monitoring and assessment due to their dependability and feasibility. …”
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Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion by Ying Chen, Yuanning Liu, Xiaodong Zhu, Huiling Chen, Fei He, Yutong Pang

“…The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples’ own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.…”
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