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IEEE Cloud Summit

作者： Chowdhury, Shusmoy Katangur, Ajay Missouri State Univ Dept Comp Sci Springfield MO 65897 USA

ISBN: (纸本)9798350322170

Cloud computing has become one of the most influential inventions in modern-day computer science. It has become one of the integral parts of our day-to-day life. The success of our regular activities relies on the efficiency and effectiveness of the cloud services. The datacenter selection policy is one of the vital elements in cloud computing. The user requests are processed in the datacenter. Therefore, an optimal datacenter ensures user satisfaction with cloud services. In this research, we propose a differential evolution-based datacenter selection policy to choose the optimal datacenter for the userbase. differential evolution mainly depends on the evolutionary process of mutation, which helps the algorithm explore the whole search space and find the optimal datacenter. We have used the CloudAnalyst simulator to experiment with the correctness and performance of our algorithm. The simulation results demonstrate that the proposed differential evolution algorithm performs better than the existing datacenter selection algorithm, such as the closest datacenter selection policy and optimized response time policy.

关键词： Cloud computing differential evolution algorithm Service Broker Policy

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Comparative Analysis of Particle Swarm Optimization and differential evolution algorithm to Curtail Power Consumption using Smart Energy Meter Analytics 2

Comparative Analysis of Particle Swarm Optimization and Diff...

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2nd IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Srivastava, Amisha Rizwan, M. Mosobi, Rinchin W. Delhi Technol Univ Dept Elect Engn Delhi 110042 India

ISBN: (纸本)9781665411202

Consumption of energy has increased multifarious in recent years and its appropriate management is the need of the hour. Energy management system makes use of various tools and technologies to monitor and optimize energy consumption in households and workplaces. Use of several stochastic and deterministic algorithms for the purpose of energy management has rapidly gained substantial importance recently owing to their flexibility, ease of implementation and efficient results. This paper presents two algorithms based on Particle Swarm Optimization (PSO) and differential evolution ( DE) algorithm for optimizing consumption of power using smart energy meter data analytics. Both are meta-heuristic algorithms that can easily solve multifaceted problems in engineering. The power consumption equation has been optimized using first PSO then DE. The validity and efficiency of the algorithm is tested using the real time data obtained from smart energy meter and a comparative study is presented. The modeling and simulation is carried on MATLAB platform and the results depict that both presented algorithms can ominously reduce the power consumption. With PSO approximately 11.5% reduction in power can be obtained and DE can reduce up to 9.4% power in best-case scenario making PSO superior to DE.

关键词： Energy Management System Particle Swarm Optimization differential evolution algorithm Meta-heuristic Techniques Smart Energy Meter

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Applying the differential evolution algorithm to Model an Equivalent Digital Power Plant 15

Applying the Differential Evolution Algorithm to Model an Eq...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Alves, André Cazita Grilo Pavani, Ahda P. de Freitas, Adriano Gomes Postgraduate Academic Program on Energy The Federal University of ABC - UFABC 5001 dos Estados Avenue Twr 1 SP Santo André09210-170 Brazil Faculty of Engineering Monash University Wellington Rd ClaytonVIC3800 Australia

ISBN: (纸本)9798350314182

Due to increased high penetration of renewable energy resources in power systems, the concept of Virtual Power Plant (VPP) has been proposed to aggregate Distributed Energy Resources (DER) to act like a single power plant. In this context, the purpose of this work is the development of a method to aggregate DER units, which use the interface with the electrical network by converters, representing them by equivalent VPPs. In addition to this representation, it is necessary to estimate the parameters of the equivalent VPP model. The method used for this purpose is based on the Gray-box method and a differential evolution (DE) algorithm, which allows the use of measurement data that can be performed at the boundary between the distribution system and the system under study. The model is validated using the MATLAB/SIMULINK software to simulate a test network and obtain parameters of the equivalent model. ©2023 IEEE.

关键词： differential evolution algorithm distributed generation distribution network equivalents distribution networks

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Optimization Analysis of Dynamic Economic Environment Scheduling Problem Based on differential evolution algorithm 1

Optimization Analysis of Dynamic Economic Environment Schedu...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Liu, Shengyuan University of California California Irvine United States

ISBN: (纸本)9798350316469

differential evolution algorithm is a common optimization algorithm with extensive applications in solving dynamic economic environment scheduling problems. The dynamic and uncertainty of economic activities make it difficult to achieve efficient resource scheduling and economic benefit maximization in the dynamic economic environment. In this paper, we study dynamic economic environment scheduling based on a differential evolution algorithm. First, the background and significance of dynamic economic environment scheduling are expounded. Then we analyze the key factors and optimization strategies of this method in the optimization process. Finally, the power generation cost results of DE algorithm are 2.4805,2.4653,2.4196 and 2.5016 (∗ 106), which are the lowest cost compared with other algorithms. The experimental results show that the optimization method of dynamic economic environment scheduling problem based on differential evolution algorithm can solve the problem effectively, and has high optimization effect and application value. The method can not only ensure the economy, but also adapt to the change of dynamic environment, with high practicability and operability. © 2023 IEEE.

关键词： differential evolution algorithm Dynamic Economic Environment Scheduling Large-Scale Constraints Multi-Objective Optimization

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English Teaching Ability Evaluation algorithm Based on differential evolution algorithm 1

English Teaching Ability Evaluation Algorithm Based on Diffe...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Zhang, Xiaoying Xinjiang University Xinjiang Urumqi China

ISBN: (纸本)9798350316469

This article first establishes an evaluation index system for English teaching ability, including teaching attitude, teaching methods, teaching level, and other aspects. Then, differential evolution algorithm is used to optimize the weight of indicators, and the teacher's English teaching ability is quantitatively evaluated based on the optimization results. The experimental results show that the English teaching ability evaluation algorithm based on differential evolution algorithm proposed in this article has good evaluation results and high feasibility, and can provide reference for English teaching ability evaluation. At the same time, this algorithm also has good scalability and can be applied to capability evaluation in other fields. © 2023 IEEE.

关键词： Ability to teach English Competency assessment differential evolution algorithm intelligence theory

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Unfolding neutron spectra from water-pumping-injection multilayered concentric sphere neutron spectrometer using self-adaptive differential evolution algorithm

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Nuclear Science and Techniques 2021年第3期32卷 41-51页

作者： Rui Li Jian-Bo Yang Xian-Guo Tuo Jie Xu Rui Shi Chengdu University of Technology Chengdu 610059China Sichuan University of Science and Engineering Zigong 643000China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu 610059China

A self-adaptive differential evolution neutron spectrum unfolding algorithm(SDENUA)is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neutron spectrometer(WMNS).Specifically,the neutron fluence bounds are estimated to accelerate the algorithm convergence,and the minimum error between the optimal solution and input neutron counts with relative uncertainties is limited to 10^(-6)to avoid unnecessary ***,the crossover probability and scaling factor are self-adaptively *** Monte Carlo is used to simulate the readings of the WMNS under(1)a spectrum of Cf-252 and(2)its spectrum after being moderated,(3)a spectrum used for boron neutron capture therapy,and(4)a reactor ***,the measured neutron counts are unfolded using the *** uncertainties of the measured neutron count and the response matrix are considered in the SDENUA,which does not require complex parameter tuning or an a priori default *** results indicate that the solutions of the SDENUA agree better with the IAEA spectra than those of MAXED and GRAVEL in UMG 3.1,and the errors of the final results calculated using the SDENUA are less than 12%.The established SDENUA can be used to unfold spectra from the WMNS.

关键词： Water-pumping-injection multilayered spectrometer Neutron spectrum unfolding differential evolution algorithm Self-adaptive control

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Research on Reliability Evaluation of Power ASIC Based on differential evolution algorithm 3

Research on Reliability Evaluation of Power ASIC Based on Di...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Xu, Changbao Yao, Hao Xin, Mingyong Chen, JunJian Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang550002 China Digital Grid Research Institute Csg Guangzhou510670 China

ISBN: (纸本)9798350382426

This paper introduces a novel reliability evaluation method for power ASIC circuits. The method utilizes the state space formula and observation formula of the power ASIC model, combined with a differential evolution algorithm to identify circuit parameters. Based on the identified parameters, the initial component state combination of the power ASIC is established. By incorporating receiver input signal amplitude and minimum circuit current thresholds, a reliability evaluation function is constructed to output the evaluation result. Experimental results demonstrate that this method effectively reduces evaluation time without compromising the accuracy of circuit reliability assessment. © 2023 IEEE.

关键词： differential evolution algorithm Power application specific integrated circuit reliability assessment

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Multi-strategy differential evolution algorithm based on adaptive hash clustering and its application in wireless sensor networks

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Bu, Xianglong Zhang, Qingke Gao, Hao Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China

Population -based algorithms aim to explore the entire solution space in global numerical optimization problems. However, it is important to acknowledge that the solution spaces of different problems possess distinct characteristics, and even distinct regions within the same solution space can vary significantly. Efficient exploration of these diverse regions necessitates the utilization of distinct search models. To address this challenge, this study proposes a new variant of the differential evolution (DE) algorithm called MHDE. The MHDE algorithm introduces hash clustering technology combined with adaptive mutation strategies. The integration of hash clustering technology enables fast population clustering, significantly enhancing clustering efficiency. Additionally, a method for evaluating the population state is designed that allows for an adaptive clustering quantity to adapt the clustering quantity to the population state. Furthermore, a novel method is devised to calculate individual improvements considering the varying levels of difficulty that individuals face in achieving improvements within a population. This method is combined with a parameter adaptive mechanism, resulting in a weighted parameter adaptive mechanism. Experiments are conducted on the CEC2017 benchmark suite to evaluate the performance of the MHDE algorithm. The experimental results demonstrate that MHDE exhibits competitive performance compared with other efficient DE variants. Moreover, the MHDE algorithm is applied to the node deployment problem in wireless sensor networks (WSNs). The MHDE algorithm demonstrates efficient performance in node deployment problems through simulation experiments in different scenarios.

关键词： differential evolution algorithm Population clustering Multiple mutation strategies Population state evaluation Wireless sensor networks

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Design Optimization of Multilayer Microwave Filter Using differential evolution algorithm

Design Optimization of Multilayer Microwave Filter Using Dif...

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9th IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)

作者： Toktas, Abdurrahim Ustun, Deniz Karamanoglu Mehmetbey Univ Engn Fac Dept Elect & Elect Engn Karaman Turkey Tarsus Univ Engn Fac Dept Comp Engn Tarsus Mersin Turkey

ISBN: (纸本)9781665450485

Three types of multilayer microwave filter (MMF) which are low-pass (LP), high-pass (HP) and band-pass (BP) are designed through a frequency-dependent material using Pareto-based multi-objective differential evolution (DE) (MODE) algorithm. To this end, a dual-objective electromagnetic (EM) model taking into account oblique incident wave angle with transverse electric (TE) or transverse magnetic (TM) polarization is constructed, which is based on total reflection (TR) for the passing and stopping bands. The global optimal designs (GODs) are selected from the Pareto optimal designs (PODs) by considering the trade-off among the objective values. The frequency and angular behavior of the MMFs are comparatively studied. The designed MMF are near ideal characteristic thanks to MODE having effective and powerful multi-objective strategy.

关键词： Microwave filter design multi-objective optimization differential evolution algorithm Pareto optimality

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Performance optimization of solid oxide electrolysis cell for syngas production by high temperature co-electrolysis via differential evolution algorithm with practical constraints

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ENERGY CONVERSION AND MANAGEMENT 2024年 300卷

作者： Fei, Yuxuan Li, Ang Zhang, Chen Tu, Hengyong Zhu, Lei Huang, Zhen Shanghai Jiao Tong Univ Key Lab Power Machinery & Engn MOE Shanghai Peoples R China Shanghai Jiao Tong Univ Coll Smart Energy Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Power Machinery & Engn Shanghai 200240 Peoples R China

Co-electrolysis of water and carbon dioxide using solid oxide electrolysis cell combined with the catalytic reactor to produce renewable synthesis fuel is considered as a promising strategy for carbon dioxide utilization. How-ever, in order to achieve a target product with a remarkable system-level efficiency, a certain proportion of carbon monoxide and hydrogen from solid oxide electrolysis cell must be sent to the catalytic reactor directly, which imposes practical constraints on the solid oxide electrolysis cell performance. Take this constraint into consideration, we proposed a performance design methodology that enables a rapid and effective optimization of the solid oxide electrolysis cell operating conditions. Firstly, a physical model is developed by integrating chemical equilibrium, electrochemistry, and energy conservation involved in the solid oxide electrolysis cell. Secondly, a physical model is utilized to generate a dataset for the development of neural network model and optimization algorithm. This approach allows for the efficient determination of optimal conditions while considering practical constraints. In the case study, under the constraint that the desired fuel is a long carbon chain fuel with a carbon dioxide conversion rate ranging from 80 % to 90 %, the highest efficiency achieved by the solid oxide electrolysis cell is optimized to 66.75 %. Moreover, this approach is extended to other two target fuels (dimethyl ether and methane) under different carbon dioxide conversion rate, and the optimal performance is designed. According to the results, there is a trade-off between high efficiency and high carbon dioxide conversion rate, as the low carbon dioxide conversion rate often leads to higher efficiency. This method allows for flexible design of operating conditions to meet different fuel requirements and guide practical applications.

关键词： Solid oxide electrolysis cell Neural network differential evolution algorithm Optimization

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