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IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia)

作者： Han, Gandong Zhang, Weiyi Niu, Liting Zhang, Chun Wang, Zhihua Wang, Ziqiang Tsinghua Univ Sch Integrated Circuits Beijing Peoples R China Tsinghua Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781665450690

Division operation is necessary for many applications, especially optimization algorithms for machine learning. Usually, a certain degree of loss is acceptable in calculating nonsignificant intermediate variables for a considerable speed improvement. This paper proposes a specialized divider to accelerate machine learning optimization algorithm implementation on hardware. Inspired by the fast inverse square root algorithm, we designed a hardware implementation method according to the algorithm, which generates an approximate division result with conversion between floating-point and fixed-point numbers and multiplication. This paper includes three versions of divider: fastDiv accuracy, a conventional design with a 35% less delay and minimal error compared to delay-minimized standard divider from the Synopsys DesignWare library;fastDiv area, an area-oriented design with a 67% less delay and acceptable error compared to the standard divider constrained to the same area size;fastDiv speed, the fastest design with a 54% less delay compared to delay-minimized standard divider. All these three versions can be applied in deploying optimization algorithms in FPGA or ASIC design on demand.

关键词： fixed-point division optimization algorithm hardware acceleration fast square root

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Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Bakir, Huseyin Dogus Univ Vocat Sch Dept Elect & Automat TR-34775 Istanbul Turkiye

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (dFDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the dFDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (dFDBARO) algorithm. Moreover, the practicability of the proposed dFDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed dFDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the dFDBARO algorithm is available at https://***/matlabcentral/filee xchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm.

关键词： A novel enhanced artificial rabbits optimization algorithm Metaheuristic algorithm design Roulette and dynamic fitness -distance balance selection methods Optimal power flow Renewable energy sources

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A quantum online portfolio optimization algorithm

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QUANTUM INFORMATION PROCESSING 2024年第3期23卷 63-63页

作者： Lim, Debbie Rebentrost, Patrick Univ Latvia Riga Latvia Ctr Quantum Technol Singapore Singapore

Portfolio optimization plays a central role in finance to obtain optimal portfolio allocations that aim to achieve certain investment goals. Portfolio optimization also provides a rich area to study the application of quantum computers to obtain advantages over classical computers. In a multi-period setting, we give a sampling version of an existing classical online portfolio optimization algorithm by Helmbold et al., for which we in turn develop a quantum version. The quantum advantage is achieved by using techniques such as quantum state preparation, inner product estimation and multi-sampling. Our quantum algorithm provides a quadratic speedup in the time complexity, in terms of n, where n is the number of assets in the portfolio. The transaction cost of both of our classical and quantum algorithms is independent of n which is especially useful for practical applications with a large number of assets.

关键词： Online algorithm optimization algorithm Portfolio optimization Quantum algorithm Quantum computing

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Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning

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APPLIED WATER SCIENCE 2024年第4期14卷 78页

作者： Costache, Romulus Pal, Subodh Chandra Pande, Chaitanya B. Islam, Abu Reza Md. Towfiqul Alshehri, Fahad Abdo, Hazem Ghassan Transilvania Univ Brasov Dept Civil Engn 5 Turnului Str Brasov 500152 Romania Danube Delta Natl Inst Res & Dev 165 Babadag St Tulcea 820112 Romania Natl Inst Hydrol & Water Management Bucuresti Ploiesti Rd 97E Bucharest 013686 Romania Univ Bucharest Res Inst 90-92 Sos Panduri Bucharest 050107 Romania Univ Burdwan Dept Geog Bardhaman 713104 West Bengal India Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Univ Tenaga Nas Inst Energy Infrastructure Kajang 43000 Malaysia King Saud Univ Coll Sci Geol & Geophys Dept Abdullah Alrushaid Chair Earth Sci Remote Sensing Riyadh Saudi Arabia Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh Daffodil Int Univ Dept Dev Studies Dhaka 1216 Bangladesh Tartous Univ Fac Arts & Humanities Geog Dept Tartous Syria

Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzau river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance approximate to 20%), distance from river (importance approximate to 17.5%), land use (importance approximate to 12%) and TPI (importance approximate to 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35-40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).

关键词： Artificial intelligence optimization algorithm Flood potential Buzau river basin Romania

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Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 43卷

作者： Jayalakshmi, P. Ramesh, S. S. Subashka SRM Inst Sci & Technol Ramapuram Dept Comp Sci & Engn Chennai Tamil Nadu India

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user's workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users' satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat optimization algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony optimization algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm optimization algorithm (QPF-PSOA), Biogeography optimization (BBO) algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-ba

关键词： Cloud-edge computing Workflow scheduling Multi-strategies improved sand cat optimization algorithm Execution latency Energy consumptions Pareto optimal solutions

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Day-Ahead Electricity Price Forecasting Strategy Based on Machine Learning and optimization algorithm 4

Day-Ahead Electricity Price Forecasting Strategy Based on Ma...

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Sun, Caixin Pan, Xiaofeng Li, Gang Li, Pengfei Gao, Guoqing Tian, Ye Xu, Gesheng Huaneng Clean Energy Res Inst Beijing Peoples R China Huaneng Grp Co Ltd Shanxi Branch Taiyuan Peoples R China Huaneng Shanxi Power Supply Co Ltd Taiyuan Peoples R China Huaneng Shanghai Renewable Corp Co Ltd Shanghai Peoples R China Xian FPA Energy Technol Co Ltd Xian Peoples R China

ISBN: (纸本)9781665479141

Electric power marketization is the product of the continuous development of the current electric power system. Day-ahead electricity price plays an important role in power market, so the prediction of day-ahead electricity price becomes very important. In this paper, the load rate of the system is introduced, and the machine learning algorithm is adopted. In addition, the improvement and expansion of the machine learning algorithm are realized by combining the tuning advantage of the bionic optimization algorithm, and then the accurate prediction of day-ahead electricity price is realized. The results show that this strategy algorithm can greatly improve the accuracy of day-ahead electricity price forecasting.

关键词： system load rate electricity price forecast machine learning optimization algorithm

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Efficient Microwave Wireless Power Transmission using optimization algorithm 16

Efficient Microwave Wireless Power Transmission using Optimi...

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16th European Conference on Antennas and Propagation (EuCAP)

作者： Kim, Ho Yeol Nam, Sangwook Seoul Natl Univ Sch Elect & Informat Engn Seoul South Korea

ISBN: (纸本)9788831299046

In this study, we developed an efficient microwave wireless power transmission (MPT) system for multiple receivers using an optimization (OPT) technique. The optimization algorithm finds the optimal transmission signal for transferring the desired power to multiple receivers with maximum power transfer efficiency (PTE). We designed a 5 x 5 rectangular patch array antenna and patch element antenna operating at 10 GHz as the transmitter and receiver, respectively. The operating process of the MPT system using the OPT technique is analyzed. Additionally, we compared the received power of each receiver and the PTE of the OPT technique with that of the multi-receiver time-reversal (MR-TR) technique considering various scenarios. The OPT algorithm generates a multibeam to charge multiple receiver simultaneously. We validated that the OPT technique can deliver power to receivers precisely at desired ratios with greater PTE than that of the MR-TR technique in an MPT system.

关键词： microwave wireless power transmission array antenna optimization algorithm

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Dynamic performance improvement of oscillating water column wave energy conversion system using optimal walrus optimization algorithm-based control strategy

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AIN SHAMS ENGINEERING JOURNAL 2024年第12期15卷

作者： Eldemery, Habiba A. Hasanien, Hany M. Alharbi, Mohammed Sun, Chuanyu Zaky, Dina A. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Harbin Inst Technol Sch Elect Engn & Automat Harbin 150006 Peoples R China Higher Inst Engn & Technol Elect Power Engn Dept Cairo Egypt

The optimal design of the proportional-integral (PI) controller using the walrus optimization algorithm (WOA) with the aim to enhance the dynamic performance of a grid-connected wave energy conversion (WEC) system when subjected to diverse operational conditions is presented in this paper. The proposed system under study consists of an oscillating water column (OWC) device coupled with a permanent magnet synchronous generator (PMSG) to supply power to the grid. Power electronics devices in the form of a generator-side converter (GSC) and a grid-side inverter (GSI) are used to couple the grid and WEC systems. The GSC is used to minimize generator losses and maximize generator real power through the control of d-axis and q-axis currents (id, and iq) of the PMSG. The GSI is used to control the point of common coupling (PCC) and DC-link voltages (VPCC,VDC), respectively. The PI controllers, used to minimize the error between the actual current and voltage values with their respective reference values, are optimally designed using the WOA. The fitness function of the optimization problem is based on the integral square error criterion (ISE). Presented in this paper is a model for the OWC-WEC system and a control strategy to maximize generated power, minimize generator losses, and keep the VDC, V PCC at required values, the usage of WOA to design the PI controllers, and the simulations of system results. The proposed WOA-based PI controller design's effectiveness is evaluated by comparing its simulation results with that obtained from using genetic algorithm (GA), grey wolf (GWO), particle swarm (PWO), and harmony search (HS) optimization-based PI controllers under symmetrical and unsymmetrical faults. The proposed strategy shows an enhancement in the dynamic performance of OWC wave energy systems when compared to the other optimization algorithm-based PI controllers, as well as achieving the least value for ISE, which reached 0.172.

关键词： optimization algorithm Walrus optimization algorithm Oscillating water column Renewable energy system Wave energy

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DeSGOA: double exponential smoothing gazelle optimization algorithm-based deep learning model for blind source separation

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Muhsina, N. Dhoulath, Beegum J. TKM Coll Engn Dept Elect & Commun Engn Kollam 691005 Kerala India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India

Blind Source Separation (BSS) pertains to a scenario, wherein the sources, as the method used for mixing are not known;only the mixed signals are accessible for subsequent separation. In several applications, it is preferable to retrieve all sources from the mixed signal or, at least isolate a specific source. The research proposes a novel approach, named Double Exponential Smoothing Gazelle optimization algorithm-based Generative Adversarial Network (DeSGOA-based GAN), and designed for BSS. The proposed algorithm, DeSGOA combines the power of Double Exponential Smoothing (DES) with the efficiency of the Gazelle optimization algorithm (GOA) to achieve superior results in source separation tasks. The research aims to enhance the accuracy and performance of BSS processes using the presented approach. At first, the mixed input signals attained from the dataset are fed to pre-processing phase. This phase aspires to eradicate noise present in the signal via the application of PCA model. The final objective is to capture a important amount of data information in the reduced dataset. Following this, the BSS process is carried out by utilizing the GAN, which is trained through the innovative DeSGOA algorithm. Experimental outcomes illustrate the efficacy of DeSGOA-based GAN method in achieving high-quality source separation, underscoring its potential as a valuable tool in audio signal processing and related applications. Finally, the experimental evaluation illustrated that the presented strategy gained SDR of 35.05, SIR of 11.94, SAR of 8.247, and ISR of 13.02.

关键词： optimization Deep learning Blind source separation Double exponential smoothing gazelle optimization algorithm Generative adversarial network-neural egg separation Principal component analysis

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LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion optimization algorithm to Maximize Network Lifetime

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Computers, Materials & Continua 2021年第10期69卷 351-371页

作者： Sankar Sennan Somula Ramasubbareddy Anand Nayyar Yunyoung Nam Mohamed Abouhawwash Department of Computer Science Engineering Sona College of TechnologySalem636005India VNR Vignana Jyothi Institute of Engineering&Technology Hyderabad500090India Graduate School Duy Tan UniversityDa NangVietnam550000Vietnam Faculty of Information Technology Duy Tan UniversityDa NangVietnam550000Vietnam Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI48824USA

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained *** is an effective technique for saving energy by reducing duplicate *** a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a ***,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH ***,early battery depletion is produced near the *** overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this ***-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean *** selection is performed using *** establishment is implemented using residual energy *** extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and *** performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and *** proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and ***-RPL is also highly energy-efficient compared with other similar routing protocols.

关键词： Internet of things cluster head clustering protocol optimization algorithm lion optimization algorithm network lifetime routing protocol wireless sensor networks energy consumption low-power and lossy networks

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