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4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhang, Jinhu Sun, Bin Sun, Jianpeng Yu, Hao Xie, Xiguo Zhengzhou University Zhengzhou China Co. Ltd. Beijing China

ISBN: (纸本)9798331531225

The performance of large-scale parallel computing applications is highly dependent on the parameter settings within complex systems. Due to the high-dimensional and nonlinear nature of kernel environment parameter spaces, traditional manual tuning methods are both labor-intensive and inefficient when exploring optimal parameter combinations, often leading to suboptimal performance. To address this issue, this paper proposes an automated parameter tuning framework based on Bayesian optimization, aimed at efficiently exploring complex high-dimensional parameter spaces and predicting better-performing parameter combinations. This framework builds a surrogate model, iteratively selects potentially efficient parameter configurations, and integrates SHAP to analyze the influence of each parameter on performance optimization. We validated the effectiveness of this framework in several typical large-scale parallel computing application scenarios, including NAS Parallel Benchmarks (NPB), quantum chemistry simulation software Quantum ESPRESSO and VASP, and the Weather Research and Forecasting model (WRF). Experimental results show that Bayesian optimization significantly shortens the overall tuning time and performs traditional methods such as genetic algorithms in terms of performance optimization. Experimental results show that Bayesian optimization significantly shortens the overall tuning time by 50% and improves application performance by up to 11%, outperforming traditional methods such as genetic algorithms in terms of performance optimization. Furthermore, SHAP analysis reveals the importance of key kernel parameters in enhancing application performance. © 2024 IEEE.

关键词： Bayesian networks

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A Pareto-Based Sparse Subspace Learning Framework

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第11期49卷 3859-3872页

作者： Luo, Juanjuan Jiao, Licheng Liu, Fang Yang, Shuyuan Ma, Wenping Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist EducInt Collaborat Joint Lab Intelligent Int Res Ctr Intelligent Percept & Computat Xian 710071 Shaanxi Peoples R China

high-dimensionality is a common characteristic of real-world data, which often results in high time and space complexity or poor performance of ensuing methods. Subspace learning, as one kind of dimension reduction method, provides a way to overcome the aforementioned problem. In this paper, we introduce multiobjective evolutionary optimization into subspace learning, and propose a Pareto-based sparse subspace learning algorithm for classification tasks. The proposed algorithm aims at minimizing two conflicting objective functions, the reconstruction error and the sparsity. A kernel trick derived from Gaussian kernel is implemented to the sparse subspace learning for the nonlinear phenomena of nature. In order to speed up the convergence, an entropy-driven initialization scheme and a gradient-descent mutation scheme are designed specifically. At last, a knee point is selected from the Pareto front to guarantee that we can obtain a solution with good classification performance, and yet as sparse as possible. The experiments and detailed analysis on real-life datasets and the hyperspectral images demonstrated that the proposed model achieves comparable results with the existing conventional subspace learning and evolutionary feature selection algorithms. Hence, this paper provides a more flexible and efficient approach for sparse subspace learning.

关键词： Classification multiobjective optimization Pareto solutions sparse representation subspace learning

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RNP AR APPROACH ROUTE optimization USING A GENETIC ALGORITHM

RNP AR APPROACH ROUTE OPTIMIZATION USING A GENETIC ALGORITHM

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Integrated Communication, Navigation and Surveillance conference (ICNS)

作者： Toratani, Daichi Mori, Ryota Elect Nav Res Inst Chofu Tokyo Japan

ISBN: (纸本)9781665484190

Route design in civil aviation highly affects the flight time and fuel consumption of a passenger aircraft. In this work, an optimization method for the approach route of the required navigation performance authorization required (RNP AR) is developed. This method provides high flexibility in designing the route. A nonlinear genetic algorithm is employed to optimize the approach route, considering the constraints of a ground surface and obstacles. Case studies demonstrating the performance of the proposed optimization method are also presented. The results show that the proposed method is capable of calculating the optimal route in each case, in accordance with reference approach routes. Additionally, the obtained results are analyzed to evaluate the optimality of the optimization results.

关键词： Authorization Surveillance optimization methods Aircraft navigation Fuels Aircraft Genetic algorithms

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Systematic performance Evaluation of Reinforcement Learning algorithms Applied to Wastewater Treatment Control optimization

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023年第46期57卷 18382-18390页

作者： Croll, Henry C. C. Ikuma, Kaoru Ong, Say Kee Sarkar, Soumik Iowa State Univ Dept Civil Construct & Environm Engn Ames IA 50011 USA Iowa State Univ Dept Mech Engn Ames IA 50011 USA

Treatment of wastewater using activated sludge relieson severalcomplex, nonlinear processes. While activated sludge systems can providehigh levels of treatment, including nutrient removal, operating thesesystems is often challenging and energy intensive. Significant researchinvestment has been made in recent years into improving control optimizationof such systems, through both domain knowledge and, more recently, machine learning. This study leverages a novel interface betweena common process modeling software and a Python reinforcement learningenvironment to evaluate four common reinforcement learning algorithmsfor their ability to minimize treatment energy use while maintainingeffluent compliance within the Benchmark Simulation Model No. 1 (BSM1)simulation. Three of the algorithms tested, deep Q-learning, proximalpolicy optimization, and synchronous advantage actor critic, generallyperformed poorly over the scenarios tested in this study. In contrast,the twin delayed deep deterministic policy gradient (TD3) algorithmconsistently produced a high level of control optimization while maintainingthe treatment requirements. Under the best selection of state observationfeatures, TD3 control optimization reduced aeration and pumping energyrequirements by 14.3% compared to the BSM1 benchmark control, outperformingthe advanced domain-based control strategy of ammonia-based aerationcontrol, although future work is necessary to improve robustness ofRL implementation. Reinforcementlearning can provide better than human controloptimization. This study evaluates reinforcement learning controloptimization strategies to reduce wastewater treatment energy costswhile maintaining effluent compliance.

关键词： machine learning operations control optimization energy efficiency BSM1 twin delayed deepdeterministic policy gradient (TD3)

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Input space adaptive design: A high-level methodology for energy and performance optimization.

Input space adaptive design: A high-level methodology for en...

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38th Design Automation conference (DAC)

作者： Wang, W Raghunathan, A Lakshminarayana, G Jha, NK Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)1581132972

This paper presents a high-level design methodology, called input space adaptive design, and new design automation algorithms for optimizing energy consumption and performance. An input space adaptive design exploits the well-known fact that the quality of hardware circuits and software programs can be significantly optimized by employing algorithms and implementation architectures that adapt to the input statistics. We propose a methodology for such designs which includes identifying parts of the behavior to be optimized, selecting appropriate input sub-spaces, transforming the behavior, and verifying the equivalence of the original and optimized designs- Experimental results indicate that such designs can reduce energy consumption by up to 70.6% (average of 55.4%), and simultaneously improve performance by up. to 85.1% (average of 58.1%), leading to a reduction in the energy-delay product by up to 95.6% (average of 80.7%), compared to well-optimized designs that do not employ such techniques.

关键词： Embedded systems

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Advanced optimization methods for power systems

Advanced optimization methods for power systems

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2014 Power Systems Computation conference (PSCC)

作者： Panciatici, P. Campi, M. C. Garatti, S. Low, S. H. Molzahn, D. K. Sun, A. X. Wehenkel, L. RTE R&D Dept Versailles France Univ Brescia Dept Informat Engn Brescia Italy Politecn Milan Dept Elect Comp Sci & Bioengn Milan Italy CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA Univ Michigan Dept EE&CS Ann Arbor MI 48109 USA Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Liege Dept EE&CS Inst Montefiore B-4000 Liege Belgium

ISBN: (纸本)9788393580132

Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixed-integer programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted.

关键词： Big Data concave programming integer programming learning (artificial intelligence) nonlinear programming parallel processing power engineering computing power system planning randomised algorithms Big Data infrastructure advanced optimization method high-performance computing machine learning mixed integer programming nonconvex optimization power system operation randomized algorithm Electronic mail Europe Linear programming optimization Planning Power systems Uncertainty Power system planning randomized algorithms Integer Programming power systems high performance Computing big data Power engineering computing electronic mail nonlinear Programming PARALLEL PROCESSING (COMPUTERS) optimization methods linear programming Uncertainty

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Augmenting high-dimensional nonlinear optimization with conditional GANs 21

Augmenting high-dimensional nonlinear optimization with cond...

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2021 Genetic and Evolutionary Computation conference, GECCO 2021

作者： Kalehbasti, Pouya Rezazadeh Lepech, Michael D. Pandher, Samarpreet Singh Stanford University StanfordCA United States

ISBN: (纸本)9781450383516

Many mathematical optimization algorithms fail to sufficiently explore the solution space of high-dimensional nonlinear optimization problems due to the curse of dimensionality. This paper proposes generative models as a complement to optimization algorithms to improve performance in problems with high dimensionality. To demonstrate this method, a conditional generative adversarial network (C-GAN) is used to augment the solutions produced by a genetic algorithm (GA) for a 311-dimensional nonconvex multi-objective mixed-integer nonlinear optimization. The C-GAN, composed of two networks with three fully connected hidden layers, is trained on solutions generated by GA, and then given sets of desired labels (i.e., objective function values), generates complementary solutions corresponding to those labels. Six experiments are conducted to evaluate the capabilities of the proposed method. The generated complementary solutions are compared to the original solutions in terms of optimality and diversity. The generative model generates solutions with objective functions up to 79% better, and with hypervolumes up to 58% higher, than the original solutions. These findings show that a C-GAN with even a simple training approach and architecture can, with a much shorter runtime, highly improve the diversity and optimality of solutions found by an optimization algorithm for a high-dimensional nonlinear optimization problem. [GitHub repository: https://***/PouyaREZ/GAN_GA] © 2021 ACM.

关键词： Genetic algorithms

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PRODUCING A VERY high-QUALITY STILL IMAGE FROM HDMAC TRANSMISSION

PRODUCING A VERY HIGH-QUALITY STILL IMAGE FROM HDMAC TRANSMI...

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INTERNATIONAL BROADCASTING CONVENTION

作者： HELSINGIUS, M KALEVO, O HAAVISTO, P NEUVO, Y Tampere Univ of Technology Finland

ISBN: (纸本)0852965478

An experimental system for grabbing high definition television pictures from HDMAC transmission is presented. The hardware part of the system is based on a video sequencer which is used for digitizing, storing, and displaying the HDMAC signal. The HDMAC decoder itself has been implemented using computer software. The system is able to receive and display approximately a 3-second-long sequence of HDTV pictures. The software part of the system employs image enhancement algorithms to produce high quality still images. The interlaced transmission format is converted to progressive and both temporal and spatial noise filtering is applied to improve the image quality in the presence of transmission noise. The algorithms depend on simple motion detection and nonlinear filtering. Examples of the received images and of the performance of the image enhancement algorithms are shown.

关键词： image enhancement algorithm Still images noise filtering Image quality Motion detection computer software drive noise

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μOpt: An Efficient Optimal Autoscaler for Microservice Applications

μOpt: An Efficient Optimal Autoscaler for Microservice Appl...

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4th IEEE International conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Incerto, Emilio Pizziol, Roberto Tribastone, Mirco IMT Sch Adv Studies Lucca Italy

ISBN: (纸本)9798350337440

Microservices are a popular architecture for cloud-based applications subject to stringent performance requirements. To effectively serve variable workloads, autoscaling allocates computational resources ideally at the lowest possible cost. Although several autoscaling techniques have already been proposed in the literature, they suffer from high computational complexity. Here we propose mu Opt as a computationally efficient model-based autoscaler for microservices. By solving a nonlinear optimization problem that embeds a layered queueing network (LQN) model, mu Opt computes optimal configurations maximizing performance while minimizing allocated resources. We validate mu Opt on a benchmark microservice application, reporting fast solution times (similar to 10(-1) s) that enable prompt reactions to highly variable workloads. Compared to a state-of-the-art autoscaler based on LQN and genetic algorithms, mu Opt achieves higher performance (similar to 6%-8%) with significantly fewer allocated resources (similar to 15%-35%) in the presence of both synthetic and real-world workloads.

关键词： Microservices Autoscaling performance Modeling optimization Layered Queueing Networks

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Application of Wave Cluster Algorithm Based on Kernel optimization in Pattern Recognition of high-Frequency Financial Data 3

Application of Wave Cluster Algorithm Based on Kernel Optimi...

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3rd International conference on Cloud Computing, Big Data Application and software Engineering, CBASE 2024

作者： Yue, Dazhi Business School Xiamen Institute of Technology Xiamen China

ISBN: (纸本)9798331506582

high-frequency financial data has the characteristics of high nonlinearity, non-stationarity and noise, which brings great challenges to traditional data mining algorithms. In this study, we propose a Wave Cluster algorithm based on kernel function optimization, which aims to improve its performance in pattern recognition of high-frequency financial data. The Wave Cluster algorithm has attracted much attention for its robustness to noise and multiresolution analysis capability, but it still has limitations when dealing with complex nonlinear patterns. In this paper, kernel-based functions are introduced to map the original high-dimensional financial data to a higher-dimensional feature space. This nonlinear transformation can effectively capture the potentially complex relationships in the data. Subsequently, the modified Wave Cluster algorithm is applied in the transformed feature space, and high-frequency trading data from the China stock market, including price, trading volume and order book information, are used in the empirical study. Experimental results show that compared with traditional methods, the proposed algorithm achieves significant improvement in the tasks of identifying market microstructure, detecting abnormal trading patterns and predicting short-term price movements. Especially during periods of high volatility, the algorithm shows greater stability and accuracy. © 2024 IEEE.

关键词： Multiresolution analysis

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