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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Global MPPT optimization for partially shaded photovoltaic systems by T. Nagadurga, V. Dhana Raju, Abdulwasa Bakr Barnawi, Javed Khan Bhutto, Abdul Razak, Anteneh Wogasso Wodajo

“…Recent heuristic algorithms, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO) and Chimp Optimization algorithm (ChOA) are employed to address the complexities associated with maximizing power output under partial shading conditions in solar PV systems. …”
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Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions by Mohd Nasrul Izzani Jamaludin, Mohammad Faridun Naim Tajuddin, Tarek Younis, Sudhakar Babu Thanikanti, Mohammad Khishe

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Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning by Xiao-bo SHI, Yin ZHANG, Shan ZHAO, Deng-ming XIAO

“…Although multiple mobile agents(MA)collaboration can quickly and efficiently complete data aggregation in wireless sensor network,the MA carrying data packages extensively increase along with a raise in the number of data source nodes accessed by MA,which causes unbalanced energy load of sensor nodes,high energy consumption of partial source nodes,and shortened lifetime of networks.The existing related works mainly focus on the objective of decreasing total energy consumption of multiple MA,without considering that rapidly energy consumption of partial source nodes has a negative effect on networks lifetime.Therefore,discrete multi-objective optimization of particle swarm algorithm was proposed,which used the total network energy consumption and mobile agent load balancing as fitness function for the approximate optimal itinerary plan in multiple mobile agent collaboration.Furthermore,the simulation result of the proposed algorithm is better than the similar algorithm in total energy consumption and network lifetime.…”
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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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MPPT Efficiency in PV Arrays under Partial Shading Conditions:A Comparative Analysis of PSO and P&O Algorithms by Rachid Bennia, Cherif Larbes, Faiza Belhachat

“…This investigation presents a comparative analysis of two established algorithms: Particle Swarm Optimization (PSO) and Perturb and Observe (P&O), evaluating their respective capabilities in GMPP identification. …”
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Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model by Vandana Tripathi, Prabhat Patel, Prashant Kumar Jain, Shailja Shukla

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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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Comparison of MPPT optimization methods for P&O and PSO solar panels to overcome partial shading by Imelda Uli Vistalina Simanjuntak, Junas Haidi, Ricky Fajar Ade Putra, Lukman Medriavin Silalahi

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KPLS Optimization With Nature-Inspired Metaheuristic Algorithms by Jorge Daniel Mello-Roman, Adolfo Hernandez

“…It was solved using nature-inspired metaheuristic algorithms: the genetic algorithm, particle swarm optimization, grey wolf optimization and the firefly algorithm. …”
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Comparative Evaluation of Traditional and Advanced Algorithms for Photovoltaic Systems in Partial Shading Conditions by Robert Sørensen, Lucian Mihet-Popa

“…This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). …”
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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 Hybrid P&O-Fuzzy-Based Maximum Power Point Tracking (MPPT) Algorithm for Photovoltaic Systems Under Partial Shading Conditions by Hamed Karimi, Alireza Siadatan, Afshin Rezaei-Zare

“…This paper proposes a new MPPT approach that combines Perturb & Observe (P&O) and Fuzzy Logic Controller (FLC) with the Particle Swarm Optimization (PSO) algorithm. The FLC algorithm is then used to maximize search accuracy. …”
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Enhanced ANN-Based MPPT for Photovoltaic Systems: Integrating Metaheuristic and Analytical Algorithms for Optimal Performance Under Partial Shading by Alpaslan Demirci, Idriss Dagal, Said Mirza Tercan, Hasan Gundogdu, Musa Terkes, Umit Cali

“…The results demonstrate that the improved ANN-based MPPT algorithm consistently outperforms existing MPPT techniques, including the Perturb and Observe (P&O) and Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO) methods. …”
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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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Hybrid precoding method for millimeter-wave massive MIMO systems based on IAFS algorithm by Haoyi CHEN, Guangqiu LI, Hui LI

“…The millimeter-wave massive multiple-input multiple-output (MIMO) systems can overcome the adverse effects of the free-space signal path loss through the partial connection hybrid precoding method, which has the advantages of low hardware complexity and high energy efficiency.When the number of input data streams is equal to the number of radio frequency (RF) links, the hybrid precoding method based on partially connected structure and serial interference cancellation can be used.When the number of input data streams is not equal to the number of RF links, a hybrid precoding method based on improved artificial fish swarm (IAFS) algorithm was proposed.The core idea is that based on the spectral efficiency optimization criteria and the characteristics of partial connected structure, the spectral efficiency optimization problem of analog recoding matrix variables was transformed into the spectral efficiency optimization problem based on vector variables, and then the IAFS algorithm was used to solve the spectrum efficiency optimization problem.The simulation results show that the proposed method has good spectral efficiency and energy efficiency under the condition of low signal-to-noise ratio, and is expected to be applied in the real scene.…”
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Comparison of Maximum Power Point Tracking Methods Using Metaheuristic Optimization Algorithms for Photovoltaic Systems by Necati Bilgin, İrfan Yazici

“…In the simulation studies, the output powers obtained by the application of particle swarm optimization, cuckoo optimization, bat optimization and firefly optimization techniques as MPPT algorithm has been compared. …”
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An innovative maximum power point tracking for photovoltaic systems operating under partially shaded conditions using Grey Wolf Optimization algorithm by Muhannad J. Alshareef

“…On the other hand, the PV system must be run at its maximum power point (GMPP) to maximize its efficiency. Swarm optimization strategies have been employed to detect the GMPP; however, these methods are associated with an unacceptable amount of time to reach convergence. …”
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The Application of Compound Control Algorithm in Photovoltaic System MPPT by WANG Shenghui, LI Yilun, ZHENG Hong, GAO Shan

“…Aiming at the problem that the output array exhibits multipeak characteristics when the PV array is partially shaded or unevenly illuminated, the traditional singlepeak MPPT algorithm is difficult to track the maximum power point A hybrid algorithm is proposed to improve the particle swarm combined with the sliding mode search Firstly, the probability judgment criterion of improved simulated annealing algorithm is introduced into the standard particle swarm optimization algorithm; the law of inertia weight change is improved; the disturbance parameter is added to the learning factor Secondly, using the sliding mode extreme value search algorithm, the suspected optimal value obtained by the particle swarm optimization algorithm is continuously optimized, and finally the maximum power point is found The simulation results show that the composite control algorithm can track the maximum power point quickly and accurately under different shadow conditions, and avoid the system falling into the local optimum value…”
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A Comparison of Heuristic Algorithms for Solving the Traveling Salesman Problem by Younes Khdeir, Ahmed Awad

“…This paper presents a comparison between four popular algorithms: steepest ascent hill climbing, simulated annealing, genetic algorithm with partially matched crossover, and Particle Swarm Optimization (PSO). …”
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