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Informatica (Slovenia) 2024年第17期48卷 209-222页

作者： Makhlouf, Amina Benmachiche, Abdelmadjid Boutabia, Ines LIMA Laboratory Dept. of Computer Science Faculty of science and technology House of Artificial Intelligence Chadli Bendjedid University El-Tarf PB 73 Algeria36000 Algeria

Determining path planning for mobile robots has received a lot of attention over the last three decades, with the goal of identifying safe and effective paths between starting points and destinations. The investigation of diverse navigation methodologies exhibits potential in expanding the range of uses for autonomous mobile robots. This research introduces a novel navigation strategy by combining the Particle Swarm optimization (PSO) method with the Bacteria Foraging optimization Algorithm (BFO). Utilizing the benefits of PSO, this bio-inspired method is used to optimize paths for mobile robots, drawing inspiration from the foraging behavior of bacteria, namely E. coli. The principal aim of this study is to design routes that are practical and safe. The proposed model features a robot that emulates bacterial foraging behavior to determine the most optimal path through environments densely populated with obstacles, effectively linking a starting point to a specified destination. This strategy has been implemented and rigorously evaluated across a variety of scenarios. Experimental results reveal that the hybrid BFO-PSO algorithm achieves a success rate exceeding 90% in navigating dynamic environments. Furthermore, the algorithm significantly outperforms traditional methods, offering approximately 50% less computational speed, efficiency, and superior security metrics, confirming its effectiveness and potential for real-world applications. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Escherichia coli

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Probabilistic Energy and Reserve Co-dispatch for High-renewable Power systems and Its Convex Reformulation

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Journal of Modern Power systems and Clean Energy 2023年第6期11卷 1734-1745页

作者： Shuwei Xu Wenchuan Wu Bin Wang Yue Yang State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China IEEE School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China

This paper proposes a probabilistic energy and reserve co-dispatch(PERD) model to address the strong uncertainties in high-renewable power systems. The expected costs of potential renewable energy curtailment/load shedding are fully considered in this model, which avoids insufficient or excessive emergency control capacity to produce more economical reserve decisions than conventional chance-constrained dispatch methods. Furthermore, an analytical reformulation approach of PERD is proposed to make it tractable. We firstly develop an approximation technique with high precision to convert the integral terms in objective functions into analytical ones. Then, the calculation of probabilistic constraints is equivalently transformed into an unconstrained optimization problem by introducing value-at-risk(Va R) representation. Specifically, the Va R formulas can be computed by a computationally-cheap dichotomy search algorithm. Finally, the PERD model is transformed into a convex problem, which can be solved reliably and efficiently using off-the-shelf solvers. Case studies are performed on IEEE test systems and real provincial power grids in China to illustrate the scalability and efficiency of the proposed method.

关键词： Renewable energy energy and reserve dispatch emergency control stochastic optimization analytical approximation

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Physics-informed deep Koopman operator for Lagrangian dynamic systems

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Science China(Information Sciences) 2024年第9期67卷 146-163页

作者： Xuefeng WANG Yang CAO Shaofeng CHEN Yu KANG Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China

Accurate mechanical system models are crucial for safe and stable control. Unlike linear systems,Lagrangian systems are highly nonlinear and difficult to optimize because of their unknown system *** research thus used deep neural networks to generate linear models of original systems by mapping nonlinear dynamic systems into a linear space with a Koopman observable function encoder. The controller then relies on the Koopman linear model. However, without physical information constraints, ensuring control consistency between the original nonlinear system and the Koopman system is tough, as the learning process of the Koopman observation function is unsupervised. This paper thus proposes a two-stage learning algorithm that uses structural subnetworks to build a physics-informed network topology to simultaneously learn the Koopman observable functions and the system energy representation. In the Koopman matrix learning session, a quadratic-constrained optimization problem is solved to ensure that the Koopman representation satisfies the energy difference matching hard constraint. The proposed energy-preserving deep Lagrangian Koopman(EPDLK) framework effectively represents the dynamics of the Lagrangian system while ensuring control consistency. The effectiveness of EPDLK is compared with those of various Koopman observable function construction methods in multistep prediction and trajectory tracking tasks. EPDLK achieves better control consistency by guaranteeing energy difference matching, which facilitates the application of the control law generated on the Koopman system directly to the original nonlinear Lagrangian system.

关键词： Koopman operator Lagrangian physics-informed nonlinear model predictive control (MPC)

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Fractional Order Iteration for Gradient Descent Method Based on Event-Triggered Mechanism

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Journal of systems Science & complexity 2023年第5期36卷 1927-1948页

作者： LU Jiajie WANG Yong FAN Yuan Department of Automation University of Science and Technology of ChinaHefei 230026China Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China

In this work,a novel gradient descent method based on event-triggered strategy has been proposed,which involves integer and fractional order ***,the convergence of integer order iterative optimization method and the stability of its associated system with integrator dynamics are *** on this result,a fractional order iteration approach has been developed by modelling the system with fractional order ***,to reduce the comsumption of computation,a feedback based event-triggered mechanism has been introduced to the gradient descent *** convergence of this new event-triggered optimization algorithm is guaranteed by using a Lyapunov method,and Zeno behavior is proved to be avoided ***,the effectiveness and advantages of the proposed algorithms are verified by numerical simulations.

关键词： Event-triggered mechanism fractional order iteration gradient descent Zeno behavior

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optimization on Photovoltaics and Energy Storage Integrated Flexible Direct Current Distribution systems of Buildings Considering Load Uncertainty Using Scenario Generation Method

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Distributed Generation and Alternative Energy Journal 2024年第2期39卷 297-318页

作者： Wu, Xiaorui Chen, Weidong Tian, Hao Yao, Zhiyang Zhao, Ergang Guo, Yongshui Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi Nanning530023 China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi Nanning530023 China Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China

This study explores the intricate challenge of energy demand uncertainty in the design of Photovoltaics and Energy Storage integrated Flexible Direct Current Distribution (PEDF) systems. Our objective is to examine the impact of different scenario generation methods on PEDF system optimization. We compare four approaches, including probabilistic techniques based on Monte Carlo simulation, Latin Hypercube Sampling for base scenario sampling, and a simulation-based method using building performance modeling. We evaluate these approaches using the Independent Scenario optimization (ISO) and Two-Stage Stochastic Programming (TSSP) models, aiming to minimize the annual total cost within PEDF systems while addressing uncertainties. Our findings shed light on the optimal PEDF design under uncertainty, offering valuable insights for future decision-making in dynamic energy systems. © 2024 River Publishers. All rights reserved.

关键词： Energy storage

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Balancing communication overhead and accuracy in compression integration: a survey

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Journal of Supercomputing 2025年第8期81卷 1-63页

作者： Yang, Aiqiang Liu, Jie Yang, Bo Mo, Zeyao Li, Keqin College of Computer Science and Technology National University of Defense Technology Changsha410000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China China Academy of Engineering Physics Beijing China Department of Computer Science State University of New York New PaltzNY12561 United States

In large-scale distributed training, communication compression techniques are widely used to reduce the significant communication overhead caused by the frequent exchange of model parameters or gradients between training nodes. However, these techniques often introduce additional computational complexity and may lead to data loss, thereby affecting model convergence and performance. This review examines key optimization methods in communication compression, including pruning techniques that remove irrelevant weights, quantization techniques that convert floating-point parameters to low-precision representations, and sparsification techniques that transmit only critical gradients. Low-rank approximation techniques, which compress parameters through matrix factorization, are particularly useful for large-scale models. These techniques have also been widely applied in various application scenarios, demonstrating their effectiveness in different environments. Application scenarios include distributed training, federated learning, and edge computing, where bottlenecks are carefully identified and evaluated in common scenarios, providing a basis for further optimization. Future development directions emphasize co-design of hardware and algorithms, dynamic strategies, and cross-layer optimization. This study provides valuable comparisons of key methods and theoretical analysis for efficient communication compression in distributed systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Edge computing

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A systematic data-driven modelling framework for nonlinear distillation processes incorporating data intervals clustering and new integrated learning algorithm

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Chinese Journal of Chemical Engineering 2025年 81卷 182-199页

作者： Wang, Zhe He, Renchu Long, Jian Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Automation China University of Petroleum Beijing102249 China

The distillation process is an important chemical process, and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling, thus improving the efficiency of process optimization or monitoring studies. However, the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals, which brings challenges to accurate data-driven modelling of distillation processes. This paper proposes a systematic data-driven modelling framework to solve these problems. Firstly, data segment variance was introduced into the K-means algorithm to form K-means data interval (KMDI) clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction. Secondly, maximal information coefficient (MIC) was employed to calculate the nonlinear correlation between variables for removing redundant features. Finally, extreme gradient boosting (XGBoost) was integrated as the basic learner into adaptive boosting (AdaBoost) with the error threshold (ET) set to improve weights update strategy to construct the new integrated learning algorithm, XGBoost-AdaBoost-ET. The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation. © 2025 Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd.

关键词： Chemical industry

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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A hybrid Intelligent Method for Path-Constrained Switched systems with Free Switching Sequences 13

A Hybrid Intelligent Method for Path-Constrained Switched Sy...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Gao, Zexiang Li, Huan Fu, Jun Zhu, Xiaochen Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang100819 China

ISBN: (纸本)9798350315196

In this article, a hybrid intelligent optimization method is proposed, which can simultaneously find the optimal input, switching instants, and switching sequences within a finite number of iterations. Firstly, the infinite-dimensional program is transformed into a finite-dimensional program through parameterization methods, and then, since the switching sequences are discrete variables, the discrete-continuous finite-dimensional program is converted into a continuous finite-dimensional program by introducing linear and quadratic constraints. Secondly, the path constraint is discretized into finite point constraints, and the right side of the constraint is replaced with a negative value. Thirdly, an improved differential evolution (IDE) is used for random search to find the global optimal region that satisfies the point constraints, and then a deterministic optimization method is used to find an approximate KKT optimal solution that rigorously satisfies path constraints in the global optimal region. Fourthly, the convergence of the hybrid intelligent method has been proven. Finally, a numerical example shows the effectiveness of the proposed method. © 2023 IEEE.

关键词： Evolutionary algorithms

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Selective laser melting of magnesium alloys: Necessity, formability, performance, optimization and applications

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Journal of Materials Science & Technology 2023年第23期154卷 65-93页

作者： Kun Li Chen Ji Shengwen Bai Bin Jiang Fusheng Pan State Key Laboratory of Mechanical Transmission Chongqing University Chongqing 400044 China Chongqing Key Laboratory of Metal Additive Manufacturing (3D Printing) Chongqing University Chongqing 400044 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing 400044 China National Engineering Research Center for Magnesium Alloys College of Materials Science and Engineering Chongqing University Chongqing 400044 China

Selective laser melting (SLM), as a rapid prototyping technology, has been widely used to manufacture high-performance metal components with complex structures, which vitally provides a broad platform for the development and application of magnesium alloys. However, the poor laser formability of magnesium alloys has deleterious consequences in the application of SLM processing. This paper discusses the defect formation mechanisms during the SLM process and summarizes characteristics in terms of mechanical properties, oxidation and corrosion resistance. Current optimization schemes are reviewed from both macro and micro perspectives. Firstly, the relationship between process parameters and formability and material properties is clarified, and advanced optimization methods of the design of experiments, physical models, and machine learning are evaluated. Secondly, the effects of alloying elements, composite reinforcement, and post-treatment on the microstructure and properties of the SLMed magnesium alloy are reviewed. Finally, the future application development prospects are envisaged based on the comprehensive review. This work is significantly helpful to a better scientific understanding of SLMed magnesium alloy and puts forward some meaningful guiding opinions for the future work of magnesium alloy manufacturing.

关键词： Magnesium alloys Selective laser melting Defects Microstructure-property relationship Performance optimization

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