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An Evolutional Topology Optimization Method Based on Kernel Level Set Function

IEEE ACCESS 2025年 13卷 47181-47200页

作者： Sato, Takahiro Watanabe, Kota Igarashi, Hajime Muroran Inst Technol Grad Sch Engn Muroran Hokkaido 0508585 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

Topology optimizations involving evolutionary algorithms are promising approaches to solve practical engineering design problems, since their use of derivation-free algorithms makes them applicable to any design problem. In some evolutional topology optimization methods, structures are determined through the use of spatially smooth parametric functions. This article, first, presents a new parametric level set function using kernel functions as a general formulation of spatially smooth function-based methods. It is called the kernel level set function, which is used to determine the distributions of materials in the design region. By using the evolutionary algorithm to modify the profile of the kernel level set function, topology optimizations can be realized for various design problems, and the shape expression ability of the kernel level set function can be customized by changing the kernel functions. Second, a multi-objective formulation considering an objective and structural complexity is introduced, and an optimization algorithm specialized for solving it is proposed. The combination of the kernel level set function and the present optimization algorithm makes it possible to solve various topology optimization problems, and Pareto fronts with respect to target performance and structural complexity can be obtained. The present method is applied to three topology optimization problems. The numerical results are used to examine the characteristics of the present method for various kernel functions. Furthermore, the present method is applied to an optimization problem of an interior permanent magnet motor for electric vehicles. It is shown that the present method can solve practical design problems that have various objectives and constraint conditions, thanks to the use of a derivation-free evolutionary algorithm.

关键词： Level set Kernel Topology Evolutionary computation linear programming Complexity theory Electric machines Finite element analysis Shape Reviews CMA-ES electric machine evolutionary algorithms topology optimization

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Onto Proximality in Non Negative Matrix Factorization for Recommender Systems

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IEEE ACCESS 2025年 13卷 67476-67487页

作者： Mehta, Rachana Mishra, Shakti Saha, Snehanshu Pandit Deendayal Energy Univ Sch Technol Comp Sci & Engn Dept Gandhinagar 382007 India Nirma Univ Inst Technol Comp Sci & Engn Dept Ahmadabad 382481 India BITS Pilani Dept Comp Sci & Informat Syst KK Birla Goa Campus Zuarinagar 403726 Goa India BITS Pilani APPCAIR KK Birla Goa Campus Zuarinagar 403726 Goa India

Recommender Systems have become integral to most e-commerce applications and online platforms. The recommended suggestions heavily impact customer retention and business performance. One of the essential parameters in large-scale recommender systems is the time required to present a recommendation. The more time it takes, the more it loses the customer's attention and interest. It is currently necessary for recommenders to be time-efficient and optimal. Collaborative filtering-based Matrix Factorization approaches have proven to be powerful for recommender systems. The standard approach uses the Singular Value Decomposition-based recommender systems with Gradient Descent optimizer and its advanced variants. These models provide good accuracy for recommenders. However, they are time-intensive. To alleviate these issues, the proximal gradient descent optimizer-based Nonnegative Matrix Factorization model is adapted for recommender systems to improve their performance in terms of time and accuracy. There has been no research on integrating proximal descent models in Nonnegative matrix factorization for recommender systems. These novel adaptations are analyzed with six other baseline recommender models on two datasets. The experimental analysis proves that these novel recommender models are the preferable choice for online recommenders and work well when the data is not smooth.

关键词： Recommender systems Optimization Matrix decomposition Adaptation models linear programming Stochastic processes Dimensionality reduction Analytical models Accuracy Principal component analysis Lipschitz continuity nonnegative matrix factorization recommender systems singular value decomposition stochastic proximal gradient descent

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An Efficient Methodology for Binary Logarithmic Computations of Floating-Point Numbers With Normalized Output Within One ulp of Accuracy

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IEEE TRANSACTIONS ON COMPUTERS 2025年第5期74卷 1800-1813页

作者： Lyu, Fei Luo, Yuanyong Liu, Weiqiang Nanjing Univ Aeronaut & Astronaut Coll Integrated Circuits Nanjing 211106 Peoples R China Minist Ind & Informat Technol Key Lab Aerosp Integrated Circuits & Microsyst Nanjing 211106 Peoples R China Nanjing Univ Sch Phys Nanjing 210093 Peoples R China Huawei Corp Dept Turing Architecture & Design Linx Lab HiSilicon Shenzhen 518129 Peoples R China

Many studies have focused on the hardware implementation of binary logarithmic computation with fixed-point output. Although their outputs are accurate within 1 ulp (unit in the last place) in fixed-point format, they are far from meeting the accuracy requirement of 1 ulp in floating-point format when the output is close to 0. However, normalized floating-point output that is accurate to within 1-3 ulp is needed in many math libraries (for example, OpenCL, NVIDIA CUDA, and AMD AOCL). To the best of our knowledge, this is the first study to propose a hardware implementation of binary logarithmic computation for floating-point numbers with a normalized output that is accurate to within 1 ulp. Instead of calculating log(2)(1 + fi) (where fi is the fractional part of the floating-point number) directly, the proposed methodology uses two novel objective functions for the polynomial approximation method. The novel objective functions make the significant bits of the outputs move forward to eliminate the necessity for high precision near zero. Compared with the designs of fixed-point binary logarithmic converters, the proposed hardware implementation achieves greater accuracy to meet the requirement of 1 ulp of floating-point format with a 21% extra area consumption.

关键词： Hardware Accuracy Polynomials Libraries Computers linear programming Mathematical models Graphics processing units Computer architecture Computational efficiency Binary logarithmic operation hardware implementation floating-point number polynomial approximation method

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Research on Container Multimodal Transportation Multi-Objective Path Optimization From Hinterlands to Shanghai Port

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IEEE ACCESS 2025年 13卷 32794-32807页

作者： Huang, Chuan Sun, Hanbing Liu, Changjian Zhang, Xiaoqing Gao, Tianhang Tian, Jia Minist Transport Transport Planning & Res Inst Beijing 100028 Peoples R China Digital Lab Integrated Transportat Planning Beijing 100028 Peoples R China Wuhan Univ Technol Wuhan 430062 Peoples R China

Given Shanghai Port's proximity to the sea and the Yangtze River, this article investigated the optimization of Shanghai Port's foreign trade container multimodal transport path. First and foremost, because the range of the port hinterland generating container is large, Sichuan Province, Chongqing Municipality, Hubei Province, Hunan Province, Jiangxi Province, Anhui Province, and Jiangsu Province are chosen as the major container generating hinterland. And the container intermodal transportation network is centered on these cities and provinces. Then, using minimizing total transportation cost and total transportation time as objective functions, a multi-objective container multimodal transportation path optimization model is built. The improved multi-objective particle swarm approach is used to solve the mathematical mode. Finally, the efficient multimodal container transportation routes from Yibin, Luzhou, Chongqing, Yi-chang, Wuhan, Jiujiang, and Yueyang to Shanghai Port are identified. The acquired results are used to discuss the port competitiveness and freight organization games. It closes by verifying the usefulness of the approaches and models presented in this study. It is intended that the study presented in this paper will serve as a model and method reference for optimizing container multimodal transportation patterns.

关键词： Transportation Containers Seaports Costs Optimization Biological system modeling linear programming Optimization models Roads Pricing Shanghai port container multimodal transportation path optimization multi-objective particle swarm optimization

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Speeding-Up Evolutionary Algorithms to Solve Black-Box Optimization Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 117-131页

作者： Echevarrieta, Judith Arza, Etor Perez, Aritz Basque Ctr Appl Math Machine Learning Dept Bilbao 48009 Spain

Population-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choose the best solutions from a given population after comparing their objective values, which are then used to generate the next population. This iterative process explores the solution space efficiently, leading to improved solutions over time. However, one of the challenges of these algorithms is that they require a large number of evaluations to provide a quality solution, which might be computationally expensive when the evaluation cost is high. In some cases, it is possible to replace the original objective function with a less accurate approximation of lower cost. This introduces a tradeoff between the evaluation cost and its accuracy. In this article, we propose a technique capable of choosing an appropriate approximate function cost during the execution of the optimization algorithm. The proposal finds the minimum evaluation cost at which the solutions are still properly ranked, and consequently, more evaluations can be computed in the same amount of time with minimal accuracy loss. An experimental section on four very different problems reveals that the proposed approach can reach the same objective value in less than half of the time in certain cases.

关键词： Costs linear programming Optimization Proposals Computational modeling Approximation algorithms Function approximation Approximate objective function computationally expensive black-box problem evaluation cost population-based evolutionary algorithm

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Precision heating strategy based dynamic heater path planning for frost protection in apple orchards

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BIOSYSTEMS ENGINEERING 2025年 250卷 117-132页

作者： Hua, Weiyun He, Long Heinemann, Paul Zhu, Minghui Penn State Univ Dept Agr & Biol Engn State Coll PA 16802 USA Penn State Univ Fruit Res & Extens Ctr Biglerville PA USA Penn State Univ Sch Elect Engn & Comp Sci State Coll PA USA

Frost is one of the severe weather events causing economic losses in agriculture. Traditionally, heating can be applied in an orchard during a frost event either using fixed heaters or mobile heaters to travel through. However, both traditional heating strategies have limited heating capacity. In this study, a precision heating strategy was proposed to prioritise applying heat to orchard canopies with high heating demands utilising multiple heaters. The precision heating experiment in a simulated orchard environment with dynamic multiple goal points was implemented by an improved A-Star path-planning algorithm, in which a path cost estimation method based on linear programming was presented to find the optimal paths for multiple heaters and a conflictbased search (CBS) method was used to generate collision-free paths. In addition, an estimation method for the heater number based on the precision heating strategy was proposed and the difference of the heater number between precision heating and traditional heating was compared. The simulated results show that the improved A-Star algorithm had higher search efficiency, resulting in 36.8% less total path cost and 98.7% less computational time than the control group. The comparison between precision heating and traditional heating indicates that the number of heaters used in the precision heating strategy decreased by 96.8% and 85.9% compared to the traditional fixed heating strategy and mobile heating strategy, respectively. Overall, this study provided a concept of high heat-demand priority precision heating and proved its superiority for frost protection.

关键词： Frost protection Precision heating strategy Dynamic multi-goal point path planning linear programming Heater number A -Star algorithm

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Optimal Stochastic Injection Attack Strategy Against Discrete Time-Varying System via Moment Matrices

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11026-11037页

作者： Gao, Sheng Gramlich, Dennis Zhang, Hao Yan, Huaicheng Ebenbauer, Christian Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 200092 Peoples R China Rhein Westfal TH Aachen Chair Intelligent Control Syst D-52074 Aachen Germany Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

This paper investigates injection attacks incorporating stochastic noise against linear discrete time-varying system, which is more general but also more challenging to defend than deterministic injection attacks. Based on the optimization theory and novel key defining matrices, an optimal stochastic injection attack strategy is proposed. Unlike existing strategies, this strategy is stochastic, making it harder for defenders to predict. Therefore, the newly designed attack is expected to be widely used to disrupt system performance. By leveraging estimated data from the observer and the attack input, a virtual residual system is established, which more accurately reflects changes in the system error before and after the attack than the traditional error system. Using the state and output residuals along with the stochastic attack input, two performances are defined to ensure the stealthiness and effectiveness of the attack, respectively. Subsequently, an optimal attack problem with non-convex objective function and constraint is formulated. The key to acquiring the designed optimal stochastic injection attack strategy is to apply a semi-definite relaxation involving moment matrices for transforming this non-convex optimization problem into a convex optimization problem and solving it. Finally, the effectiveness of the proposed attack strategy is validated through numerical simulation of a networked mass-spring-damper system and a V-formation experiment involving three quadrotors. Note to Practitioners-The primary objective of this paper is to focus on the cyber security of discrete time-varying systems from the perspective of the attacker, which provides insight into the way of generating attack strategies under the stochastic case. The majority of existing injection attack strategies against intelligent systems are deterministic, and this can make the attacks less effective as the attacked system reconstructs and compensates for the attack signals, and the attack s

关键词： Time-varying systems Optimization Observers linear programming programming Estimation error Numerical simulation Convex functions Control systems Automation Stochastic injection attack non-convex optimization problem moment matrices semi-definite programming

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Age-Based Multi-Channel-Scheduling Under Constraints: Optimal and Online Designs

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IEEE TRANSACTIONS ON NETWORKING 2025年第1期33卷 51-64页

作者： Zhou, Xujin Koprulu, Irem Eryilmaz, Atilla Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

We study the optimal scheduling problem where $n$ source nodes attempt to transmit updates over L shared wireless on/off fading channels to optimize their age performance under energy and age-violation tolerance constraints. Specifically, we provide a generic formulation of age-optimization in the form of a constrained Markov Decision Process (CMDP), and obtain the optimal scheduler as the solution of an associated linear programming problem. We investigate the characteristics of the optimal single-user multi-channel scheduler under different age-related objectives where a usual threshold-based policy does not apply. We then investigate the stability region of the optimal scheduler for the multi-user case under age-violation tolerance constraints. Furthermore, we develop two online schedulers that do not require statistics and are amenable to scalable operation: Drift-plus-penalty-based design, and a novel variation of the well-known Q-learning-based reinforcement learning method that combines Q-learning with drift-minimization-methods successfully for the first time, to the best of our knowledge. Our numerical studies compare the performance of our online schedulers to the optimal scheduler to reveal that both algorithms capture the essential behavior of the optimal design under different scenarios with good scalability, with the Q-learning-based design providing even closer performance to the optimal one by utilizing the history of the drift in a novel way.

关键词： Optimal scheduling Measurement Wireless communication Internet of Things linear programming Fading channels Q-learning Costs Standards Receivers Multi-channel scheduling drift-plus-penalty method age-of-information (AOI) constrained Markov decision processes (CMDP) reinforcement learning

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Unsupervised Deep Lagrange Dual With Equation Embedding for AC Optimal Power Flow

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第1期40卷 1078-1090页

作者： Kim, Minsoo Kim, Hongseok Sogang Univ Dept Elect Engn Seoul 04107 South Korea

Conventional solvers are often computationally expensive for constrained optimization, particularly in large-scale and time-critical problems including AC optimal power flow (OPF) problems. While this leads to a growing interest in using neural networks (NNs) as fast optimal solution approximators, incorporating the constraints with NNs is challenging. In this regard, we propose deep Lagrange dual with equation embedding (DeepLDE), a framework that learns to find an optimal solution without using labels. To ensure feasible solutions, we embed equality constraints into the NNs and train the NNs using the primal-dual method to impose inequality constraints. The equality constraints correspond to power flow equations, and the inequality constraints include the operational limits of generators and transmission lines. We prove the convergence of DeepLDE and show that the previous primal-dual learning method cannot solely ensure equality constraints without the help of equation embedding. Simulation results on non-convex and AC-OPF problems show that the proposed DeepLDE achieves the smallest optimality gap among all the NN-based approaches while always ensuring feasible solutions. Furthermore, the computation time of the proposed method is up to 35 times faster than the baselines in solving constrained non-convex optimization, and/or AC-OPF.

关键词： Artificial neural networks Optimization Mathematical models Training Load flow linear programming Unsupervised learning Deep learning Neural network AC optimal power flow

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Index Tracking via Sparse Bayesian Regression and Collaborative Neurodynamic Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第3期55卷 1238-1249页

作者： Zhang, Fangyu Wang, Jun City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Dept Data Sci Hong Kong Peoples R China

Index tracking is a primary passive investment strategy. Many existing methods, such as cardinality-constrained and regularized regressions, need to prespecify parameters to generate sparse portfolios to track indices, which complicates the tracking procedure and may compromise tracking performance. This article addresses index tracking and enhanced index tracking via Bayesian learning and collaborative neurodynamic optimization. Specifically, we formulate a sparse Bayesian regression problem for index tracking. Furthermore, we reformulate the problem for enhanced index tracking by adding constraints based on a second-order stochastic domination rule. To overcome the nonconvexity of the objective function in the formulated problems, we propose a sparse Bayesian regression algorithm based on multiple recurrent neural networks in the collaborative neurodynamic optimization framework. We demonstrate the superiority of the proposed methods to mainstream baselines in terms of predictability, consistency, sparsity, and profitability via experimentation on the data from seven major stock markets.

关键词： Indexes Portfolios Optimization Bayes methods Target tracking Neurodynamics Vectors Collaboration Stochastic processes linear programming Collaborative neurodynamic optimization enhanced index tracking index tracking recurrent neural network sparse Bayesian regression

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