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Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Jamaludin, Mohd Nasrul Izzani Tajuddin, Mohammad Faridun Naim Younis, Tarek Thanikanti, Sudhakar Babu Khishe, Mohammad Univ Malaysia Perlis Fac Elect Engn & Technol Arau 02600 Malaysia Aswan Univ Dept Elect Engn Aswan 81528 Egypt Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Jadara Univ Res Ctr Irbid Jordan

The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques. As long as the rate of change of irradiance does not exceed a specific limit, the HC mode is applied to track the global maximum power point (GMPP). Once a high rate of change in irradiation is detected, the SSA mode is activated. Moreover, the proposed algorithm employs the concept of boundary conditions to handle fast and slow fluctuating irradiance patterns. A comprehensive comparative evaluation of the proposed hybrid SSA-HC with state-of-the-art MPPT algorithms has been undertaken. Four distinct cases have been examined, including irradiance conditions with varying rates of change and partial shading conditions. The proposed hybrid SSA-HC algorithm has been validated and tested using a developed hardware setup, simulated in MATLAB for solar photovoltaic (PV) systems, and compared with standard SSA and HC. The performance of the tracking capability of this proposed hybrid technique at both steady-state and dynamic conditions under rapid and gradual irradiance changes demonstrates its superiority over recent state-of-the-art algorithms.

关键词： Maximum power point tracking optimization algorithms Fluctuating environments Partial shading Solar PV system

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Efficient Hyper-parameter optimization with Cubic Regularization 37

Efficient Hyper-parameter Optimization with Cubic Regulariza...

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37th Conference on Neural Information Processing Systems (NeurIPS)

作者： Shen, Zhenqian Yang, Hansi Li, Yong Kwok, James Yao, Quanming Tsinghua Univ Dept Elect Engn Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781713899921

As hyper-parameters are ubiquitous and can significantly affect the model performance, hyper-parameter optimization is extremely important in machine learning. In this paper, we consider a sub-class of hyper-parameter optimization problems, where the hyper-gradients are not available. Such problems frequently appear when the performance metric is non-differentiable or the hyper-parameter is not continuous. However, existing algorithms, like Bayesian optimization and reinforcement learning, often get trapped in local optimals with poor performance. To address the above limitations, we propose to use cubic regularization to accelerate convergence and avoid saddle points. First, we adopt stochastic relaxation, which allows obtaining gradient and Hessian information without hyper-gradients. Then, we exploit the rich curvature information by cubic regularization. Theoretically, we prove that the proposed method can converge to approximate second-order stationary points, and the convergence is also guaranteed when the lower-level problem is inexactly solved. Experiments on synthetic and real-world data demonstrate the effectiveness of our proposed method.

关键词： optimization algorithms

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An Efficient Antenna optimization Method Based on Variable Population Size 12

An Efficient Antenna Optimization Method Based on Variable P...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Peng, Fengling Chen, Xing Sichuan University Sichuan Chengdu610065 China

ISBN: (纸本)9798350351019

To reduce the computational cost and time associated with automatic antenna optimization, this paper proposes a differential evolution algorithm with a variable population mechanism. Firstly, the paper introduces the differences between traditional methods and the variable population approach. Subsequently, three metrics are designed as criteria to implement a population size adjustment mechanism. This mechanism includes an individual addition mechanism and an individual deletion mechanism. Experimental results show that the variable population method can significantly reduce the number of fullwave simulations required for antenna optimization. © 2024 IEEE.

关键词： optimization algorithms

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optimization Algorithm Synthesis based on Integral Quadratic Constraints: A Tutorial 62

Optimization Algorithm Synthesis based on Integral Quadratic...

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62nd IEEE Conference on Decision and Control (CDC)

作者： Scherer, Carsten W. Ebenbauer, Christian Holicki, Tobias Univ Stuttgart Dept Math Stuttgart Germany Rhein Westfal TH Aachen Chair Intelligent Control Syst Aachen Germany

ISBN: (纸本)9798350301243

We expose in a tutorial fashion the mechanisms which underlie the synthesis of optimization algorithms based on dynamic integral quadratic constraints. We reveal how these tools from robust control allow to design accelerated gradient descent algorithms with optimal guaranteed convergence rates by solving small-sized convex semi-definite programs. It is shown that this extends to the design of extremum controllers, with the goal to regulate the output of a general linear closed-loop system to the minimum of an objective function. Numerical experiments illustrate that we can not only recover gradient decent and the triple momentum variant of Nesterov's accelerated first order algorithm, but also automatically synthesize optimal algorithms even if the gradient information is passed through non-trivial dynamics, such as time-delays.

关键词： optimization algorithms Robust Control Linear Matrix Inequalities

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Improving power system performance by integrating TCSC and SVC devices in the presence of stochastic renewable energy generators

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WIND ENGINEERING 2025年

作者： Benhabsa, Imadeddine Kouadri, Ramzi Slimani, Linda Mouassa, Souhil Univ Set 1 Elect Engn Dept Bez Campus Setif 19137 Algeria Univ Bouira Elect Engn Dept Bouira Algeria

This paper investigates the application of advanced metaheuristic algorithms Blood-Sucking Leech Optimizer (BSLO), Bonobo Optimizer (BO), and Electric Eel Foraging optimization (EEFO) to solve the optimal power flow (OPF) problem with stochastic renewable energy generators (REGs), specifically photovoltaic power generators (PVGs) and wind power generators (WGs). Two scenarios are examined: Scenario 1 evaluates the proposed algorithms performance without Flexible AC Transmission Systems (FACTS), focusing on minimizing Total Generation Cost (TGC), Active Power Loss (APL), and a combined objective of TGC and Emissions (TGCE). The TGC including both thermal and REG costs, in which the cost related to stochastic PV and wind power generation encompasses direct, reserve, and penalty costs due to overestimation and underestimation of available PV and wind power. Scenario 2 introduces the Thyristor-Controlled Series Capacitor (TCSC) and the Static Var Compensator (SVC) to evaluate their impact on three objective functions. The performance of the algorithms is evaluated on a modified IEEE 30-bus system. The results show that the BSLO algorithm consistently achieves the best TGC, APL, and TGCE values at 781.1209 $/h, 1.9960 MW, and 810.7376 $/h, respectively. These outcomes highlight its effectiveness and competitive performance in the first scenario. The integration of FACTS devices in the second scenario results in a 6.73% reduction in APL with the insertion of TCSC, a 1.86% reduction with the insertion of SVC, and a 6.10% reduction with the insertion of both TCSC and SVC, compared to the APL value in the case without FACTS devices (1.9960 MW). The study comprehensively analyzes how different optimization techniques and FACTS devices enhance power system performance with stochastic REG integration.

关键词： Optimal power flow stochastic PV and wind power generation thyristor-controlled series capacitor the static var compensator blood-sucking leech optimizer optimization algorithms

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Reliability-Aware Network Slicing based on Multi-Objective optimization 13

Reliability-Aware Network Slicing based on Multi-Objective O...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xia, Qiqi Wang, Jin Huang, Chengqiang Yao, Xin Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China Huawei Technologies Co. Ltd. Reliability Technology Lab Shenzhen China Lingnan University Department of Computing and Decision Sciences Hong Kong

ISBN: (纸本)9798350308365

Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric. © 2024 IEEE.

关键词： optimization algorithms

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A PRELIMINARY STUDY ON ACCELERATING SIMULATION optimization WITH GPU IMPLEMENTATION

A PRELIMINARY STUDY ON ACCELERATING SIMULATION OPTIMIZATION ...

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2024 Winter Simulation Conference

作者： He, Jinghai Liu, Haoyu Wu, Yuhang Zheng, Zeyu Zhu, Tingyu Univ Calif Berkeley Dept Ind Eng & Operat Res Berkeley CA 94720 USA

ISBN: (纸本)9798331534202;9798331534219

We provide a preliminary study on utilizing GPU (Graphics Processing Unit) to accelerate computation for three simulation optimization tasks with either first-order or second-order algorithms. Compared to the implementation using only CPU (Central Processing Unit), the GPU implementation benefits from computational advantages of parallel processing for large-scale matrices and vectors operations. Numerical experiments demonstrate such computational advantages of utilizing GPU implementation in simulation optimization problems, and show that such advantage comparatively further increase as the problem scale increases.

关键词： optimization algorithms

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Di-AEFA: Diversified Artificial Electric Field Algorithm for Global Numerical optimization 8

Di-AEFA: Diversified Artificial Electric Field Algorithm for...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Feda, Afi Kekeli Adegboye, Oluwatayomi Rereloluwa Tibetan, Abosede Omowumi Ojekemi, Opeoluwa Seun Oz, Tolga Ulker, Ezgi Deniz European University of Lefke Advanced Research Centre Mersin-10 Turkey Management Information System University of Mediterranean Karpasia Mersin-10 Turkey Occupational Health and Safety University of Mediterranean Karpasia Mersin-10 Turkey Engineering Management University of Mediterranean Karpasia Mersin-10 Turkey University of Mediterranean Karpasia Aviation Management Department Mersin-10 Turkey European University of Lefke Mersin-10 Turkey

ISBN: (纸本)9798331540104

The Artificial Electric Field Algorithm (AEFA) is categorized as one of the strong algorithms in the area of metaheuristics. It is influenced by Coulomb's law of the electrostatic force. Although it is known as an acceptable algorithm, AEFA is susceptible to early convergence because of its inherent properties, such as the strong attraction force that exist between particles. This paper proposes a diversified AEFA algorithm (Di-AEFA) which is a variation of the original AEFA to form a solid balance between exploration and exploitation. Using a set of 17 strong static benchmark functions, Di-AEFA was assessed and contrasted against a variety of state-of-art algorithms, such as Particle Swarm optimization (PSO), Cuckoo Search (CS), Firefly Algorithm (FA), Salp Swarm Algorithm (SSA) and Differential Evolution (DE). Experimental and statistical results demonstrate that Di-AEFA produces quite acceptable results and performs better than the algorithms tested. Additionally, the performance of Di-AEFA is validated using five dynamic benchmark functions, and it was compared with six powerful algorithms. Overall, the results reveal that Di AEFA performs better than the original AEFA and the counterpart algorithms © 2024 IEEE.

关键词： optimization algorithms

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A Transparent and Flexible Polarization Conversion Electromagnetic Scatterer

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第12期23卷 4912-4916页

作者： Zhen, Qiutong Mao, Zhoutao Chen, Hongzhi Chen, Jing Chernogor, Leonid F. Jin, Zhejun Sun, Zhongsen Zheng, Yu Zhou, Peiheng Liu, Tian Qingdao Univ Coll Elect Informat Qingdao 266071 Peoples R China Univ Elect Sci & Technol China Natl Engn Res Ctr Electromagnet Radiat Control Mat Key Lab Multispectral Absorbing Mat & Struct Minist Educ Chengdu 610054 Peoples R China Jilin Univ Coll Elect Sci & Engn Int Ctr Future Sci State Key Lab Integrated Optoelect Changchun 130012 Peoples R China

This letter presents a novel design for a transparent and flexible electromagnetic scatterer aimed at reducing radar cross section (RCS). Made from low-resistance indium tin oxide (ITO) thin film, PET, and PVC plastic, it achieves up to 81.3% transparency. The innovative unit design overcomes ITO's conductivity limitations and broadens bandwidth. Using the Pancharatnam-Berry phase principle, rotating the top-layer conductive structure creates encoded supercells. The letter explores RCS optimization using multiple algorithms, analyzes RCS changes in metasurface scatterer (MSS) with ITO patches in the conformal state, and compares the effects of ITO and copper on RCS performance. Multialgorithm optimization results in over 10 dB RCS reduction from 8.5 GHz to 19.8 GHz, even under 2 pi curvature or 45 degrees oblique incidence. With a compact size (0.135 lambda(0)), transparency(81.3%), flexibility(central angle up to 2 pi), and wideband performance (80%), the scatterer is ideal for applications like multispectral scattering, antenna radomes, and transparent windows.

关键词： Indium tin oxide Scattering Metasurfaces Wideband Reflection optimization Encoding Simulation Reflection coefficient Bending optimization algorithms Pancharatnam-Berry (PB) phase polarization conversion radar cross section (RCS) transparent and flexible scatterer

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GPGPU AND SIEVE OF SUNDARAM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2024年第4期2024卷 31-42页

作者： Ghidarcea, Mircea Popescu, Decebal UNST POLITEHNICA of Bucharest – Computer Science Romania

In the quasi-forgotten universe of prime sieving, Sieve of Sundaram is seen as an elegant approach to prime generation, but its performance is significantly worse than basically every other algorithm but brute-force trial-division. Yet, exactly this less than stellar arithmetic complexity makes it an ideal candidate to demonstrate the power of GPU based computing in tackling this kind of problems that evade the usual matrix oriented, symmetrical jobs for which the GPGPU paradigm was natively devised. This paper intends to demonstrate how a well thought approach can benefit from the immense throughput of modern GPUs to solve problems like sieving, which are not perfectly symmetric. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： optimization algorithms

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