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International Conference on Innovative Trends in Computer Engineering (ITCE)

作者： Saleh, Ayat Ali Mohamed, Al-Attar Ali Hemeida, A. M. Ibrahim, Abdalla Ahmed Aswan Univ Fac Energy Engn Aswan Egypt Aswan Univ Fac Engn Aswan Egypt

ISBN: (纸本)9781538652619

In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during reduction of single and multi-objective function namely, (network power losses, voltage deviation, and total operating cost). The multi objective function is formed by the use of weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i. e., unity and optimal) with and without capacitor (C). WOA technique has been applied to a 33-bus radial distribution system. The performance of the WOA technique is compared with other evolutionary optimization methods under different operating conditions of the system. The impact of integrating the proper size of DG and C at the suitable placement based on proposed algorithm are shown in the simulation results.

关键词： optimization algorithm Radial Distribution system Distributed generation Capacitor banks Power loss reduction voltage deviation improvement total operating cost reduction

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A Mixed Integer Efficient Global optimization algorithm with Multiple Infill Strategy - Applied to a Wing Topology optimization Problem

A Mixed Integer Efficient Global Optimization Algorithm with...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Roy, Satadru Crossley, William A. Stanford, Bret K. Moore, Kenneth T. Gray, Justin S. Purdue Univ Aeronaut & Astronaut 701 W Stadium Ave W Lafayette IN 47907 USA NASA Langley Res Ctr Aeroelastic Branch 218 Dodd Blvd Hampton VA 23681 USA DB Consulting Grp Inc Cleveland OH 44135 USA NASA Glenn Res Ctr Cleveland OH 44135 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed a new optimization algorithm, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previously perceived. The effort here builds upon this previously developed optimization algorithm to include multiple infill sampling capability that combines the concept of generalized expected improvement function, unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGO with the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficult engineering design optimization problems. The results reveal the performance of the new approach is problem dependent. When applied to a ten-bar truss problem, the newly proposed multiple infill strategy consistently leads to a better design solutions when compared to the existing CPTV method (implemented with the context of the AMIEGO algorithm). On the other hand, when applied to a mixed-integer high fidelity wing topology optimization problem - MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferior solution as compared to CPTV approach. These results also reveal that a small number of starting points, in general, are sufficient to lead to a good overall solution.

关键词： optimization algorithm Numerical optimization Trusses Multi Objective Evolutionary algorithms High Performance Computing Surrogate Model Wingbox Kernel Functions Parallel Computing Aspect Ratio

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Modelling and optimization algorithm for Dynamic Volume of Access to VOD Business in Ubiquitous Wireless Environment 11

Modelling and Optimization Algorithm for Dynamic Volume of A...

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IEEE 11th International Conference on Communication Software and Networks (ICCSN)

作者： Zhang, Hui Sun, Yejun Lou, Yaxiang Zhao, Haitao Zhu, Hongbo Sun, Yanfei Soochow Univ NJUPT Prov Key Lab Comp Informat Proc Technol NJUPT Natl Engn Res Ctr Communicat & Networking Jiangsu Key Lab Wireless Commun Nanjing Peoples R China NJUPT Minist Educ Engn Res Ctr Hlth Serv Syst Based Ubiquitous Wire Nanjing Peoples R China

ISBN: (纸本)9781728121840

In the wireless ubiquitous environment, this paper analyses and models the dynamic volume of access to VOD business. This paper also proposes a wireless business optimization algorithm that enables the CDN edge server to help the core network to divert pressure of user's access best. Firstly, a probability model of the daily volume of access is defined, based on video's score and video's online time. Further, a function relationship between the parameter of the model and the joint conditions above is established to accurately reflect the average value of the daily volume of access. On this basis, the predicted value of the daily volume of access to any video under a certain guarantee probability is obtained. Then, an optimization model for distribution and download of videos is built for the edge server of CDN with limited storage space. A distribution and download scheme of videos based on 0-1 knapsack algorithm is proposed. The simulation results verify the correctness of the model of volume of access and the effectiveness of the optimization algorithm.

关键词： wireless ubiquitous environment CDN VOD business dynamic modelling optimization algorithm

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A variable relationship excavating based optimization algorithm for solving 0-1 knapsack problems 15

A variable relationship excavating based optimization algori...

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15th International Conference on Computational Intelligence and Security (CIS)

作者： Zheng, Minyi Gu, Fangqing Chen, Xuesong Wu, Hao-Tian Guangdong Univ Technol Guangzhou Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781728160924

The past decades have seen an extensive investigation of evolutionary algorithms. The recombination operators of most evolutionary algorithms are either single-point crossover or multi-point crossover for solving 0-1 combinatorial optimization problems. There is a little studies on excavating the variable relationship to improve the efficiency of the recombination operator. Hence, we propose an optimization algorithm based on excavating variable relationship for solving 0-1 combinational problems. The aim of the recombination operator is fully utilizing the inherent information from every slice component of all individuals. We compared the proposed algorithm with the classic evolutional algorithm with single-point crossover operation on several 0-1 knapsack problems, which is a classical combinatorial optimization problem. The simulation results show the convergence efficiency of the proposed algorithm.

关键词： optimization algorithm Knapsack problem Knowledge excavating Combinatorial optimization

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An Improved Animal Migration optimization algorithm Based on Interactive Learning Behavior for High Dimensional optimization Problem

An Improved Animal Migration Optimization Algorithm Based on...

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International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Lai, Zhaolin Feng, Xiang Yu, Huiqun East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728104669

Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, the performance of AMO is degraded rapidly when the dimensionality is larger than 30. In order to overcome this shortcoming, an improved animal migration algorithm (IAMO) based on interactive learning behavior is proposed in this paper. First, we introduce an interactive learning behavior that individuals will learn from each other by exchanging information. During the search process, the search step is dynamically adjusted. In this case, the intelligence of IAMO is higher than AMO. Second, a refined search method is used to search around the current solutions, and this method can enhance the search ability of the algorithm. Third, a birth-and-death mechanism is designed to avoid local optimum. The effectiveness of IAMO is verified on 100 dimensional benchmark functions, and the empirical results show that the performance of IAMO is promising.

关键词： optimization algorithm migration interactive learning birth-and-death mechanism high dimension

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Fault diagnosis optimization algorithm based on k nearest neighbor 31

Fault diagnosis optimization algorithm based on k nearest ne...

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31st Chinese Control And Decision Conference (CCDC)

作者： Yang Yinghua Wang Xiulong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728101057

In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor reconstruction (FVR-kNN) has been proposed. However, dealing with fault problem caused by multiple variables, the algorithm needs to exhaustive the arrangement of all variables, resulting in high complex computation. And the multivariate estimation in FVR-KNN is not accurate. Thus, this paper proposes a variable recognition optimization algorithm based on FVR-kNN (OFVR-kNN). It optimizes the estimation steps of FVR-kNN in reconstructing multivariate, guaranteeing that the estimations of these potential fault variables have no mutual influence. According to the fault magnitude in corresponding direction, the fault variables are selected in turn. OFVR-kNN does not need to exhaustive all the combinations, greatly reducing the number of reconstructions in fault sample. In this paper, the validity of the optimization method is proved in Tennessee Eastman process.

关键词： Fault recognition KNN optimization algorithm Tennessee Eastman process (TEP)

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optimization algorithm for IPTV Video Service Delivery over SDN Using MEC Technology 18th

Optimization Algorithm for IPTV Video Service Delivery over ...

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18th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems (NEW2AN) / 11th Conference on Internet of Things and Smart Spaces (ruSMART)

作者： Manariyo, Steve Khakimov, Abdukodir Pyatkina, Darya Muthanna, Ammar St Petersburg State Univ Telecommun 22 Prospekt Bolshevikov St Petersburg 193232 Russia RUDN Univ Peoples Friendship Univ Russia 6 Miklukho Maklaya St Moscow 117198 Russia

ISBN: (纸本)9783030011680;9783030011673

Management of video content distribution through files allocation or caching in content delivery networks with some degree of reliable security measures is representing a big issue in video service delivery and user's requirements for quality of experience provision are constantly tightened. Operators are looking for new ways to efficiently deliver video content to specific customers or classes of customers, which allow the transfer of large amounts of traffic with the appropriate quality of experience. Mobile Edge Computing (MEC), initiated as an Industry Specification Group (ISG) within ETSI, is quickly gaining traction as a disruptive technology that promises to bring applications and content closer to the network edge, a move that will reduce latency and make new services optimization possible. The aim of this thesis is to provide optimization algorithms for accessing IPTV video services in managed way over Software-defined Networking (SDN) to meet the high Quality of service (QoS) reducing network latency and, ultimately, improving the end consumer's quality of experience (QoE). We also show the positive impact of SDN network using our algorithm noticeably reducing video delay.

关键词： IPTV Video traffic Software-Defined network (SDN) Mobile Edge Computing (MEC) Quality of Experience (QoE) optimization algorithm

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Multiobjective multistage robust integer optimization model and algorithm for oilfield development planning

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 159卷 107497-107497页

作者： Yan, Sen Ji, Xiaoyu Fang, Yanjun Sun, Hongguo Renmin Univ China Sch Business Beijing 100872 Peoples R China Daqing Oilfield Co Ltd Explorat & Dev Res Inst Daqing 163712 Peoples R China

This paper discusses the long-term planning problem of oilfield development. The planning aims to deploy the workload of stimulation treatments with uncertain indicators. And the uncertainties of initial stimulation effect, annual effect error and new recoverable reserves are considered. With their uncertainty sets and the adaptability of decisions, a multiobjective multistage robust integer optimization model is constructed. In this model, the total development cost is minimized and the total new recoverable reserves is maximized. And the model makes decisions on the workload of each stimulation treatment in each year under constraints of annual oil production and workload balance. In addition, an algorithm for solving multiobjective multistage robust integer optimization model is proposed, which can obtain the finitely adaptive robust efficient solution set. Finally, a numerical example of long-term oilfield development planning is presented. The planning model with given uncertainty sets is solved, to verify the validity of the proposed model and algorithm.

关键词： Oilfield development planning Multiobjective robust optimization Multistage robust optimization Integer programming optimization algorithm

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A hybrid GA-PSO optimization algorithm for conformal antenna array pattern synthesis

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2018年第13期32卷 1601-1615页

作者： Liang, Zhipeng Ouyang, Jun Yang, Feng Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Sichuan Peoples R China

This paper introduces a hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) dedicated to pattern synthesis of conformal antenna arrays. Taking advantages of both methods, the proposed hybrid GA-PSO optimization algorithm has fast convergence speed and high convergence accuracy when applied to antenna array pattern synthesis. To show the performance of the hybrid optimization algorithm, several typical test functions and optimization examples of a linear array pattern synthesis are illustrated. Finally, a 4x2 cylindrical conformal microstrip antenna array as a practical synthesis example is studied to demonstrate the proposed algorithm. The simulated and measured results have shown the proposed method is effective and reliable for conformal antenna array pattern synthesis.

关键词： optimization algorithm pattern synthesis conformal antenna array

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Test and optimization of a control algorithm for demand-oriented operation of CHP units using hardware-in-the-loop

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APPLIED ENERGY 2021年 294卷 116974-116974页

作者： Haase, Patrick Thomas, Bernd Reutlingen Univ Reutlingen Ctr Energy Res REZ Alteburgstr 150 D-72762 Reutlingen Germany

This paper covers test and verification of a forecast-based Monte Carlo algorithm for an optimized, demandoriented operation of combined heat and power (CHP) units using the hardware-in-the-loop approach. For this purpose, the optimization algorithm was implemented at a test bench at Reutlingen University for controlling a CHP unit in combination with a thermal energy storage, both in real hardware. In detail, the hardwarein-the-loop tests are intended to reveal the effects of demand forecasting accuracy, the impact of thermal energy storage capacity and the influence of load profiles on demand-oriented operation of CHP units. In addition, the paper focuses on the evaluation of the content of energy in the thermal energy storage under practical conditions. It is shown that a 5-layer model allows to determine the energy stored quite accurately, which is verified by experimental results. The hardware-in-the-loop tests disclose that demand forecasting accuracies, especially electricity demand forecasting, as well as load profiles strongly impact the potential for CHP electricity utilization on-site in demand-oriented mode. Moreover, it is shown that a larger effective capacity of the thermal energy storage positively affects demand-oriented operation. In the hardware-in-the-loop tests, the fraction of electricity generated by the CHP unit utilized on-site could thus be increased by a maximum of 27% compared to heat-led operation, which is still the most common modus operandi of small-scale CHP plants. Hence, the hardware-in-the-loop tests were adequate to prove the significant impact of the proposed algorithm for optimization of demand-oriented operation of CHP units.

关键词： optimization algorithm Demand-oriented Monte Carlo method CHP unit Hardware-in-the-Loop Thermal energy storage

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