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Planar Antenna Arrays Beamforming Using Various optimization algorithms

IEEE ACCESS 2023年 11卷 68486-68500页

作者： Ghattas, Nancy Ghuniem, Atef M. M. Abdelsalam, Abdelazeem A. A. Magdy, Ahmed Suez Canal Univ Elect Engn Dept Ismailia 41522 Egypt

Due to the importance of beamforming in improving the communication systems performance, this paper presents a novel study of beamforming of planar antenna arrays (PAAs) utilizing the Improved Grey Wolf optimization (I-GWO) algorithm with the goal of minimizing the peak sidelobe level (PSLL). It is very important to suppress the sidelobe level (SLL) because it minimizes interference and received noise. A two-dimensional (2D) optimization method is presented to find the optimal amplitude excitations and element placements in PAA. The effectiveness of beamforming optimization using the I-GWO is illustrated by comparing it with different metaheuristic algorithms such as Particle Swarm optimization (PSO), Gravitational Search Algorithm (GSA), Hybrid Particle Swarm optimization with Gravitational Search Algorithm (PSOGSA), Runge Kutta Optimizer (RUN), Slime Mould Algorithm (SMA), Harris Hawks optimization (HHO), as well as the original Grey Wolf Optimizer (GWO). Simulation findings show that antenna array beamforming using I-GWO is effective using the 2D optimization method compared to the other algorithms, where the 2D technique achieved the most decreased SLL with the fewest array elements, which helps reduce the cost of the entire system. This clearly shows that I-GWO is very efficient and can be applied to solve different beamforming optimization problems. It can also be used for the radiation pattern synthesis of other antenna array geometries for different wireless networks applications.

关键词： Beamforming grey wolf optimizer optimization algorithms planar antenna arrays sidelobe level minimization smart antennas

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Adaptive and Improved Multi-population Based Nature-inspired optimization algorithms for Water Pump Station Scheduling

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WATER RESOURCES MANAGEMENT 2020年第9期34卷 2869-2885页

作者： Turci, de O. Luca Wang, Jingcheng Brahmia, Ibrahim Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

A common problem that the world faces is the waste of energy. In water pump stations, the situation is not different. Employees still use the traditional, manual, and empirical operation of the water pumps. This process gradually generates unwanted losses of energy and money. To avoid such profligacy, this paper presents two Adaptive and one Improved Multi-population based nature-inspired optimization algorithms for water pump station scheduling. The main goal here is to obtain the optimal operational scheduling of each group of pumps, wasting the minimum amount of energy. Therefore, since the objective function relies on the shaft power consumption of all the pumps running together, our aim becomes feasible. We implemented and tested the algorithms in the main water pump station of Shanghai, in China. Based on traditional multi-population based nature-inspired optimization algorithms, such as Genetic Algorithm (GA), Ant Colony optimization (ACO), and Particle Swarm optimization (PSO), this work adapts and improves the models to fit the complex constraints and characteristics of the system. It also compares and analyses the performance of each method used in this case study, considering the obtained results. The method which demonstrated outperformance was chosen as the best solution for the present problem.

关键词： Water pump scheduling optimization optimization algorithms Optimize power consumption Constraints

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A methodology for comparing optimization algorithms for auto-tuning

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 159卷 489-504页

作者： Willemsen, Floris-Jan Schoonhoven, Richard Filipovic, Jiri Torring, Jacob O. van Nieuwpoort, Rob van Werkhoven, Ben Leiden Univ Niels Bohrweg 1 NL-2333 CA Leiden Netherlands Netherlands eSci Ctr Sci Pk 402 NL-1098 XH Amsterdam Netherlands Ctr Wiskunde & Informat Sci Pk 123 NL-1098 XG Amsterdam Netherlands Masaryk Univ Inst Comp Sci Sumavska 416-15 Brno 60200 Czech Republic Norwegian Univ Sci & Technol Hogskoleringen 1 N-7034 Trondheim Norway

Adapting applications to optimally utilize available hardware is no mean feat: the plethora of choices for optimization techniques are infeasible to maximize manually. To this end, auto -tuning frameworks are used to automate this task, which in turn use optimization algorithms to efficiently search the vast searchspaces. However, there is a lack of comparability in studies presenting advances in auto -tuning frameworks and the optimization algorithms incorporated. As each publication varies in the way experiments are conducted, metrics used, and results reported, comparing the performance of optimization algorithms among publications is infeasible. The auto -tuning community identified this as a key challenge at the 2022 Lorentz Center workshop on auto -tuning. The examination of the current state of the practice in this paper further underlines this. We propose a community -driven methodology composed of four steps regarding experimental setup, tuning budget, dealing with stochasticity, and quantifying performance. This methodology builds upon similar methodologies in other fields while taking into account the constraints and specific characteristics of the auto -tuning field, resulting in novel techniques. The methodology is demonstrated in a simple case study that compares the performance of several optimization algorithms used to auto -tune CUDA kernels on a set of modern GPUs. We provide a software tool to make the application of the methodology easy for authors, and simplifies reproducibility of results.

关键词： Auto-tuning Methodology optimization algorithms Performance optimization Performance comparison

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ON THE EXTENSION OF CONSTRAINED optimization algorithms FROM DIFFERENTIABLE TO NONDIFFERENTIABLE-PROBLEMS

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SIAM JOURNAL ON CONTROL AND optimization 1983年第2期21卷 179-203页

作者： POLAK, E MAYNE, DQ WARDI, Y UNIV CALIF BERKELEY ELECTR RES LABBERKELEYCA 94720 UNIV LONDON IMPERIAL COLL SCI & TECHNOL DEPT COMP & CONTROLLONDON SW7 2BTENGLAND

This paper presents three general schemes for extending differentiable optimization algorithms to nondifferentiable problems. It is shown that the Armijo gradient method, phase-I–phase-II methods of feasible directions and exact penalty function methods have conceptual analogs for problems with locally Lipschitz functions and implementable analogs for problems with semismooth functions. The exact penalty method has required the development of a new optimality condition.

关键词： optimization algorithms nondifferentiable optimization locally Lipschitz functions constrained optimization phase I–phase II methods

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Improving Combinatorial optimization algorithms through Record Keeping in Constructive Multistart Search

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2014年第9期29卷 864-879页

作者： King, Charles R. Tamir, Dan E. McKenney, Mark Texas State Univ Dept Comp Sci San Marcos TX 78666 USA So Illinois Univ Dept Comp Sci Edwardsville IL 62026 USA

Constructive multistart search algorithms are commonly used to address combinatorial optimization problems;however, constructive multistart search algorithm performance is fundamentally affected by two factors: (i) The choice of construction algorithm utilized and (ii) the rate of state space search redundancy. Construction algorithms are typically specific to a particular combinatorial optimization problem;therefore, we first investigate construction algorithms for iterative hill climbing applied to the traveling salesman problem and experimentally determine the best performing algorithms. We then investigate the more general problem of utilizing record-keeping mechanisms to mitigate state space search redundancy. Our research shows that a good choice of construction algorithm paired with effective record keeping significantly improves the quality of traveling salesmen problem solutions in a constant number of state explorations. Particularly, we show that Bloom filters considerably improve time performance and solution quality for iterative hill climbing approaches to the traveling salesman problem. (C) 2014 Wiley Periodicals, Inc.

关键词： Improving Combinatorial optimization algorithms Constructive Multistart Search optimization algorithms Climbing Combinatorial optimization algorithms constructivity SEARCHING ALGORITHM Constructive Algorithm travelling salesman problems bloom state space

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A Survey on Demand Response Programs in Smart Grids: Pricing Methods and optimization algorithms

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2015年第1期17卷 152-178页

作者： Vardakas, John S. Zorba, Nizar Verikoukis, Christos V. Iquadrat Barcelona Spain Qatar Univ Doha Qatar Telecommun Technol Ctr Catalonia CTTC Barcelona Spain

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

关键词： Smart grid demand response pricing methods optimization algorithms

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A review on the cosine modulated filter bank studies using meta-heuristic optimization algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第3期52卷 1629-1653页

作者： Ozdemir, Gokcen Karaboga, Nurhan Erciyes Univ Dept Elect & Elect Engn TR-38039 Kayseri Turkey

There has been a significant increase in the number of designs based on optimization techniques as the usage areas of computers have increased day by day. In this paper, Cosine Modulated Filter Banks (CMFBs) using meta-heuristic optimization techniques are reviewed. The basic features of the meta-heuristic optimization algorithms which used in related studies and the purpose of these algorithms in CMFB design are explored. The paper begins with a definition of CMFBs and continues with meta-heuristic algorithms used in CMFB studies. Later, the meta-heuristic algorithms used to design the CMFBs are briefly described. Finally, it is reviewed where algorithms are used in the CMFB designs. This study aims to clarify the role of meta-heuristic algorithms in CMFB design. The main contribution of the study to the literature is not just describing meta-heuristic algorithms, the proposed methods in the literature to design the CMFB are fully described and compared.

关键词： Cosine modulated filter bank FIR filter Heuristic optimization algorithms Hybrid optimization

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Comparison of optimization algorithms for interferometric synthetic aperture radar phase unwrapping based on identical Markov random fields

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷

作者： Zhou, Lifan Chai, Dengfeng Xia, Yu Ma, Peifeng Sch Comp Sci & Engn Changshu Inst Technol Changshu Jiangsu Peoples R China Zhejiang Univ Coll Earth Sci Hangzhou Zhejiang Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Hong Kong Hong Kong Peoples R China

Phase unwrapping (PU) is one of the key processes in measuring the elevation or deformation of the Earth's surface from its interferometric synthetic aperture radar (InSAR) data. PU problems may be formulated as maximum a posteriori estimation estimations of Markov random field (MRF). The key issue of this formulation is energy minimization. Iterated conditional mode (ICM), graph cuts (GC), loopy belief propagation (LBP), and sequential tree-reweighted message passing (TRW-S) have been proposed for the energy minimization. Unfortunately, they differ in the formulation of the MRF model for PU, which raises the question of how they compare against each other on the same MRF model for PU. We address this by investigating the four optimization algorithms and comparing them on an identical MRF model, which gives researchers some guidance as to which optimization method is best suited for solving the PU problem. Experiments using simulated and real-data illustrate that the GC algorithm is clearly the winner among the four algorithms in all cases. The ICM algorithm, although very rapid, performs much worse than the other three especially in the terrain with violent changes or discontinuities. The two message-passing algorithms-LBP and TRW-S-perform completely differently. The LBP algorithm performs surprisingly poorly on solving phase discontinuities issue, whereas the TRW-S algorithm does quite well (second only to the GC algorithm). (C) The Authors.

关键词： interferometric synthetic aperture radar Markov random fields optimization algorithms phase unwrapping

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A comparison of different optimization algorithms for retrieving aerosol optical depths from satellite data: an example of using a dual-angle algorithm

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2011年第24期32卷 8949-8968页

作者： Mu, Xihan Shen, Qingfeng Li, Zhao-Liang Yan, Guangjian Sobrino, Jose Antonio Beijing Normal Univ Sch Geog Beijing Key Lab Remote Sensing Environm & Digital State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China CNRS UMR7005 TRIO LSIIT F-67412 Illkirch Graffenstaden France Univ Valencia Image Proc Lab E-46980 Paterna Spain Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

optimization techniques are often used in remote sensing retrieval of surface or atmospheric parameters. Nevertheless, different algorithms may exhibit different performances for the same optimization problem. Comparison of some classic optimization approaches in this article aims to select the best method for retrieving aerosol opacity, or even for other parameters, from remotely sensed data. Eight frequently used optimization algorithms were evaluated using both simulated data and actual AATSR (advanced along track scanning radiometer) data. Several typical land cover types and aerosol opacity levels were also considered in the simulations to make the tests more representative. It was observed that the absolute error in retrieval would rise after a certain number of iterations due to the round-off error, and the algorithms showed different performances in the inversions without any a priori knowledge. When combined with reasonable a priori knowledge, the selection of various algorithms only slightly affected the retrieval accuracy. Given a summary of all the comparison tests, a special class named 'trust-region methods' (TR) was demonstrated to be the optimal choice in general cases. In contrast, some widely used optimization methods in aerosol research, for example, the Levenberg-Marquardt (LM) algorithm, seemed not to display a persuasive performance.

关键词： optimization algorithms absolute error optimization methods algorithms AATSR simulated data retrieval Satellite Imagery

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Distributed Incremental Cost Consensus-Based optimization algorithms for Economic Dispatch in a Microgrid

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IEEE ACCESS 2020年 8卷 12933-12941页

作者： Wang, Aijuan Liu, Wanping Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China

The economic dispatch problems (EDPs) in a microgrid (MG) have been extensively investigated by a variety of emerging algorithms. In this paper, we propose two newly distributed dynamic optimization algorithms to respectively study the EDPs under both cases without and with generation constraints under a directed topology network. Two novel dynamic optimization algorithms are based on the distributed incremental cost consensus (ICC), where the mismatch between total demand and power generation is considered. Our algorithms only require the weight matrix of the directed network to be row stochastic. The theoretical analysis on the convergence of the proposed algorithms is presented by using the small gain theorem. It can be found that the algorithms are convergent at the geometric rate. Meanwhile, the power output of the generators are proved to achieve the optimal solution of EDPs based on the proposed algorithms. Finally, the corresponding conditions are also derived, and simulation studies illustrate the correctness of our results.

关键词： Economic dispatch problem optimization algorithms smart grid incremental cost consensus geometric convergence

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