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5th International Scientific and Technical conference on Mechanical Science and Technology Update, MSTU 2021

作者： Matrenin, P.V. Kiryanova, N.G. Novosibirsk State Technical University Novosibirsk Russia Sirius University of Science and Technology Sochi Russia

The solution of optimization problems is essential for the design and control of technical systems. The optimization problem arising in practice has a high dimension, nonlinear criteria, and constraints. There are a lot of continuous optimization tasks for testing and research of optimization algorithms performance. These tasks have a convex range of acceptable values limited to a specified range for each parameter. The problem of generating test multidimensional continuous optimization tasks with nonlinear constraints and splitting the feasible region is considered. A method was proposed for splitting the feasible region by separate domains using the multidimensional grid of forbidden solutions. As a result, the problem acquires properties closer to optimizing technical systems with complex constraints. The method allows creating an unlimited number of test optimization problems, which can be used to research and develop optimization algorithms. The method is simple to implement, and the impact on the computational complexity of tasks is insignificant. Research has been carried out on four widely used continuous single-objective optimization test functions, with the Genetic algorithm and the Particle Swarm optimization algorithm. It is shown that the proposed method has an influence on the process of solving multidimensional continuous optimization problems by population algorithms and on the dependence of the accuracy of the algorithm on its heuristic coefficients. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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Placing Wireless Chargers with Multiple Antennas 19

Placing Wireless Chargers with Multiple Antennas

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19th Annual IEEE International conference on Sensing, Communication, and Networking (IEEE SECON)

作者： Dai, Haipeng Zhao, Yue Wang, Weijun Gu, Rong Qu, Yuben Lin, Chi Xu, Lijie Dou, Wanchun Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Univ Gottingen Inst Informat Gottingen Germany Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Elect Spectrum Space Minist Ind & Informat Technol Nanjing Peoples R China Dalian Univ Technol Sch Software Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116621 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Jiangsu Peoples R China

ISBN: (数字)9781665486439

ISBN: (纸本)9781665486439

This paper studies the problem of Wireless ChArger PlacemeNt with Multiple (Directional) Antennas (WANDA). Given a set of wireless chargers wherein each charger is equipped with multiple directional antennas and a set of wireless rechargeable sensors, determining the chargers' positions and the antennas' orientations, such that the overall charging utility is maximized To address WANDA, we first present a piecewise constant function to approximate the nonlinear relationship between charging power and charging distance. Then, we propose an area discretization technique to reduce the infinite solution space to a limited one without performance loss. Next, we present approximation algorithms for both Relative Orientation Fixed (WANDA-ROF) and Relative Orientation Unfixed (WANDA-ROU) situations. For WANDA-ROF, we propose a Maximum Coverage Set extraction method that transforms WANDA-ROF into the problem of maximizing a monotone submodular function subject to a partition matroid constraint and then present a 1/2 - epsilon approximation algorithm. For WANDA-ROU, we construct a candidate position set for each charger to limit the searching space. Then, we propose a novel two-level submodular optimization scheme to address it, which achieves an approximation ratio of 1/6 - epsilon. Simulation and experimental results show that our algorithms outperform comparison algorithms by at least 22%.

关键词： Wireless communication Wireless sensor networks Directional antennas Transforms Directive antennas Approximation algorithms Partitioning algorithms

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Two algorithms for fast mitigating multi-congestion in software defined networks 21

Two algorithms for fast mitigating multi-congestion in softw...

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21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Sun, Meng Zhang, Xinchang Zhang, Jianwei Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9781728120584

Although software defined networks (SDN) can achieve flexible network management, it lacks an efficient congestion control mechanism. Most existing researches focused on single link congestion scenario and has been inferior in the congestion mitigation time because of numerous flow operations between controllers and switches. Thus, in this paper, we address the problem of fast mitigating congestion (FMCP) in multiple link congestion (Multi-Congestion) scenario over SDN. We first formulate FMCP as a 0-1 nonlinear programming model, which aims to minimize the congestion mitigation cost and avoid congestion propagation. Then we propose two algorithms: one is an optimal algorithm based on Lagrange relaxation, called FMC-LR, which aims to transform the original optimization problem to several shortest path sub-problems corresponding to a new link weight;the other is a heuristic algorithm, called FMC-HE, which aims to reroute the congested flows traversing the maximum number of congested links to an alternative path with the minimum congestion mitigation cost until the congestion is mitigated. Extensive emulation results show that FMC-HE mitigates congestion faster than FMC-LR in small-scale networks, whereas FMC-LR outperforms FMC-HE in terms of success ratio and both algorithms can avoid congestion propagation. © 2019 IEEE.

关键词： Heuristic algorithms

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Reprint of Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method (REPRINTED)

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COMMUNICATIONS IN nonlinear SCIENCE AND NUMERICAL SIMULATION 2015年第1-3期21卷 225-237页

作者： D'Ambra, Pasqua Tartaglione, Gaetano CNR Inst High Performance Comp & Networking ICAR Via P Castellino 111 I-80131 Naples Italy

Image segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Image segmentation Variational models Non-linear iterative methods Multigrid solvers

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Task Scheduling algorithms for Energy optimization under Scheduling Duration and Reliability Constraints 22

Task Scheduling Algorithms for Energy Optimization under Sch...

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22nd IEEE International conference on Industrial Informatics, INDIN 2024

作者： Li, Shizhuang Hao, Bide Yang, Shichun Duan, Qianlong Chen, Wenlong Zhou, Fan School of Transportation Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798331527471

high-performance domain controllers' hardware and software are put to the test by the growing array of additional capabilities found in smart, connected cars. In smart connected automobiles, the energy consumption of high-performance domain controllers is contributing to the whole vehicle's energy consumption at an increasing rate, even though hardware systems' computing capacity is also expanding quickly. Thus, it is crucial to research reducing processor energy consumption while maintaining system performance and dependability. This paper investigates the problem of energy-optimized task scheduling in an on-board operating system for SOA-oriented architectures by combining the DVFS technique and the DAG task model under the constraints of scheduling duration and reliability. Firstly, the energy-optimal task scheduling problem for heterogeneous multicore systems is described, focusing on the constraints of scheduling duration and reliability. The shortcomings of traditional task scheduling algorithms are also analyzed. Secondly, a task scheduling algorithm based on the meta-heuristic Whale optimization Algorithm (WOA) is proposed. This algorithm includes the design of encoding, decoding, and constraint processing schemes, and assigns processing units and corresponding DVFS levels to each task in the DAG task set to achieve energy consumption optimization while satisfying constraints. To address the suboptimal performance of the Whale optimization Algorithm in high-dimensional problems, a multi-strategy optimization approach is introduced. This enhanced algorithm incorporates chaotic mapping, adaptive nonlinear convergence factors, dynamic inertia coefficients, the Lévy flight strategy, and the evolutionary population dynamics strategy. Finally, the effectiveness of the proposed algorithm is validated through simulation experiments. © 2024 IEEE.

关键词： Constraint satisfaction problems

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Energy-Aware AI-based Optimal Cloud Infra Allocation for Provisioning of Resources 27

Energy-Aware AI-based Optimal Cloud Infra Allocation for Pro...

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27th IEEE/ACIS International conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Agomuo, Okechukwu Clement Jnr, Osei Wusu Brempong Muzamal, Junaid Hussain Univ Utah Dept Operat & Informat Syst Salt Lake City UT 84132 USA Univ Utah Khalert Sch Comp Salt Lake City UT USA Fast NUCES Fast Sch Comp Lahore Pakistan

ISBN: (纸本)9798350391961;9798350391954

This paper presents a comprehensive study on optimizing resource allocation in cloud computing environments using an ensemble of machine learning techniques and optimization algorithms. We developed a multifaceted approach, integrating Long Short-Term Memory (LSTM) networks for forecasting resource demands, Particle Swarm optimization (PSO) for initial resource allocation, Q-learning for dynamic resource adjustment, and Linear Regression (LR) for predicting energy consumption. Our LSTM model demonstrated high accuracy in demand forecasting, with detailed performance metrics indicating its effectiveness in diverse scenarios. The PSO algorithm significantly enhanced the efficiency of resource distribution, evidenced by a reduction in the number of utilized units. Q-learning contributed to the system's adaptability, optimizing resource allocation based on changing demands in real-time. The LR model accurately predicted energy consumption, aligning closely with observed data and highlighting the potential for energy-efficient cloud management.

关键词： Cloud Computing Resource Allocation Machine Learning LSTM Particle Swarm optimization Q-learning Linear Regression Energy Efficiency optimization algorithms Cloud Resource Management

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Common-case computation: A high-level technique for power and performance optimization

Proceedings - Design Automation Conference

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Proceedings - Design Automation conference 1999年 56-61页

作者： Lakshminarayana, Ganesh Raghunathan, Anand Khouri, Kamal S. Jha, Niraj K. Dey, Sujit Princeton Univ San Diego United States

This paper presents a design methodology, called common-case computation (CCC), and new design automation algorithms for optimizing power consumption or performance. The proposed techniques are applicable in conjunction with any high-level design methodology where a structural register-transfer level (RTL) description and its corresponding scheduled behavioral (cycle-accurate functional RTL) description are available. It is a well-known fact that in behavioral descriptions of hardware (also in software), a small set of computations (CCCs) often accounts for most of the computational complexity. However, in hardware implementations (structural RTL or lower level), CCCs and the remaining computations are typically treated alike. This paper shows that identifying and exploiting CCCs during the design process can lead to implementations that are much more efficient in terms of power consumption or performance. We propose a CCC-based high-level design methodology with the following steps: extraction of common-case behaviors and execution conditions from the scheduled description, simplification of the common-case behaviors in a stand-alone manner, synthesis of common-case detection and execution circuits from the common-case behaviors, and composing the original design with the common-case circuits, resulting in a CCC-optimized design. We demonstrate that CCC-optimized designs reduce power consumption by up to 91.5%, or improve performance by up to 76.6% compared to designs derived without special regard for CCCs.

关键词： Computer aided design

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Experiments on software Error Prediction Using Decision Tree and Random Forest algorithms 18

Experiments on Software Error Prediction Using Decision Tree...

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18th conference on Computer Science and Intelligence Systems, FedCSIS 2023

作者： Bluemke, Ilona Borsukiewicz, Pawel Institute of Computer Science Warsaw University of Technology Nowowiejska 15/19 Warsaw00-665 Poland

ISBN: (纸本)9788396744784

Machine learning algorithms are widely used in the assessment of error-proneness in software. We conducted several experiments with error prediction on public PROMISE repository. We used Decision Tree and Random Forest algorithms. We also examined techniques aiming at the improvement of performance and accuracy of the model - such as oversampling, hyperparameter optimization or threshold adjustment. The outcome of our experiments suggests that Random Forest algorithm, with 100 - 1000 trees, can be used to obtain high values of evaluation parameters such as accuracy and balanced accuracy. However, it has to be implemented with a set of techniques countering imbalance of the datasets used to assure high values of precision and recall that correspond with correct detection of erroneous software. Additionally, it was shown that the oversampling and hyperparameter optimization could be reliably applied to the algorithm, while threshold adjustment technique was not found to be consistent. © 2023 Polish Information Processing Society.

关键词： Decision trees

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Research on Dynamic performance and Parameter optimization of the high-Speed Pantograph and Catenary System 8

Research on Dynamic Performance and Parameter Optimization o...

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2017 Prognostics and System Health Management conference (PHM-Harbin)

作者： Wang, Xinying Nian, Xiaohong Chu, Xiaoyan Yue, Pengchao Cent S Univ Coll Informat Sci & Engn Changsha Hunan Peoples R China China Southern Power Grid Peak & Frequency Regulat Power Plants Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538603703

The model of the pantograph and catenary system is established based on the commercial finite element analysis software ANSYS. And the dynamic performance of the pantograph-catenary system at different speed is simulated. Meanwhile, the reliability of the simulation model was verified by comparing the contact force with the test data. In order to enhance the dynamic performance and quality of the current collection of the pantograph-catenary system, based on the model, uniform design method is used to optimize the parameters. According to the simulation results, it shows that the system vibration increases with the speed increasing. The relationship between the standard deviation of the contact force and the parameters of the pantograph is nonlinear. The uniform design method can be used to optimize the parameters of the system to increase the quality of the current collector and providing references for developing high performance pantograph.

关键词： pantograph-catenary system dynamic performance uniform design parameter optimization

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Task Scheduling Strategy in the Cloud Based on Cauchy Chaotic Harris Hawk optimization

Task Scheduling Strategy in the Cloud Based on Cauchy Chaoti...

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2022 International conference on Cloud Computing, Big Data Applications and software Engineering, CBASE 2022

作者： Wang, Hao Li, Yaoxin Ma, Yunshen Shandong University of Science and Technology Department of Computer Science and Technology Huangdao Qingdao China

ISBN: (纸本)9798350396393

The widespread use of cloud computing in different domains brings many challenges such as resource scheduling, load balancing, power consumption, and security. To achieve high performance of cloud resources, an efficient scheduling algorithm is required to distribute jobs among available resources to maintain system balance and fast response to user tasks. This study presents a four-level cloud scheduling model and a better Harris Hawk optimization algorithm for scheduling, called the Cauchy Chaotic Harris Hawk optimization algorithm (CCHHO), to address the multi-objective scheduling problem in the cloud. First, a dynamic spiral function is used to improve the exploration phase of the standard HHO algorithm. Then, a nonlinear exponential energy decreasing strategy is used to balance the exploration to exploitation transition. Finally, we use a Cauchy chaotic mutation strategy to avoid local optimum conditions and maximize resource utilization and execution time. We describe the CCHHO implementation in detail and demonstrate through simulation-based experiments that the proposed CCHHO outperforms various existing meta-heuristics in terms of convergence speed and accuracy when looking for the best task scheduling plan in the cloud. © 2022 IEEE.

关键词： Scheduling algorithms

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