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An Improved Teaching-learning-based optimization for Multilevel Thresholding Image Segmentation

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8371-8396页

作者： Jiang, Ziqi Zou, Feng Chen, Debao Kang, Jiahui Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China

Due to its successful application image processing or computer vision system, image segmentation plays a significant role and has become a hot research hotspot. In this paper, we propose an improved teaching-learning-based optimization (NFDR-TLBO) to segment grayscale images via multilevel thresholding. In the proposed teaching-learning-based optimization variant, the neighborhood topology is introduced into the original teaching-learning-based optimization algorithm to maintain the exploration ability of the population and the fitness-distance-ratio mechanism is introduced into the original teaching-learning-based optimization algorithm to improve its optimization performance on complex numerical optimization problems. Moreover, the experimental results on 18 typical benchmark functions with different characteristics verify the feasibility and effectiveness of the proposed algorithm. Furthermore, the proposed algorithm is used to optimize Kapur entropy function in order to find the optimal threshold for image segmentation. Finally, the experimental simulations on different benchmark images show that the proposed algorithm is effective and efficient in improving the image segmentation performance in terms of peak-signal-to-noise ratio, structure similarity index and feature similarity.

关键词： Teaching– learning-based optimization Fitness distance ratio Ring neighborhood topology Image segmentation

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A learning-based robust optimization framework for synchromodal freight transportation under uncertainty

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 195卷

作者： Filom, Siyavash Razavi, Saiedeh McMaster Univ Civil Engn Dept Hamilton ON Canada

Synchromodal freight transport is characterized by its inherent dynamicity, necessitating the need for optimal decision-making in the presence of uncertainties in the real world. However, most prior research has overlooked the complexities of uncertainty modeling, often relying on assumed probability distributions that may not accurately reflect real-world conditions. This study presents a learning-based robust optimization framework for synchromodal freight transportation to derive data-driven explainable decisions. The study proposes a predict-then- optimize framework, using a combination of the Bayesian Neural Network with uncertainty quantification and dynamic robust optimization modules to solve the shipment matching problem under the synchromodality concept. The integration of prediction and optimization modules is achieved through scenario-based adjustable uncertainty sets. Rather than generating a single optimal solution, this framework produces an optimal policy based on various scenarios, enabling decision-makers to evaluate trade-offs and make informed decisions. The framework is implemented for the Great Lakes region containing nine intermodal terminals using real-world data and the performance is evaluated under various scenarios. In addition, a preprocessing heuristic-based feasible path generation algorithm is developed that helps the framework to maintain linear solution time. Numerical experiments performed on large demand instances (up to 700 shipment requests) demonstrate that the upstream prediction module significantly impacts the downstream optimization module. This effect is primarily due to variations in road travel times across scenarios, which impact transshipment operations, storage, and delay costs.

关键词： Synchromodal freight transportation learning-based optimization Uncertainty quantification Sustainability Great lakes

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A learning-based optimization Algorithm: Image Registration Optimizer Network

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Wang, Jia Wang, Ping Li, Biao Gao, Yinghui Zhao, Siyi Natl Univ Def Technol Coll Elect Sci & Technol Key Lab ATR Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Domest Fdn Software Engn Res Ctr Changsha 410073 Peoples R China

Remote sensing image registration is valuable for image-based navigation system despite posing many challenges. As the search space of image registration is usually nonconvex, the optimization algorithm, which aims to find the optimal parameters in search space, is a challenging step. Conventional optimization algorithms can hardly reconcile the contradiction of rapid convergence and global optimization. In this letter, a novel learning-based optimization algorithm named image registration optimizer network (IRON) is proposed, which can predict the global optimum straightforwardly. The IRON is trained by a 3-D tensor (9 x 9 x 9) which consists of similar metric values. Each value of the 3-D tensor corresponds to the initial parameters' 9 x 9 x 9 neighbors in the search space. The 3-D tensor's label is a vector which points to the global optimal parameters from the initial parameters. Because of the special design, our IRON could predict the global optimum directly. The experimental results demonstrate that the proposed algorithm performs better than other classical optimization algorithms as it shows higher accuracy, lower root-of-mean-square error (RMSE), and more convergence efficiency. The code is publicly available at https://***/jaxwangkd04/IRON.

关键词： Measurement Iron Image registration Tensors Prediction algorithms Deep learning Training Deep learning image registration learning-based optimization optimization

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An Energy-Efficient Multimode Multichannel Gas-Sensor System With learning-based optimization and Self-Calibration Schemes

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第3期67卷 2402-2410页

作者： Park, Kyeonghwan Choi, Subin Chae, Hee Young Park, Chan Sam Lee, Seungwook Lim, Yeongjin Shin, Heungjoo Kim, Jae Joon Ulsan Natl Inst Sci & Technol Sch Elect & Comp Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Sch Mech Aerosp & Nucl Engn Ulsan 44919 South Korea

This paper presents an energy-efficient intelligent multisensor system for hazardous gases, whose performance can be adaptively optimized through a multimode structure and a learning-based pattern recognition algorithm. The multimode operation provides control capability on the tradeoff relationship between accuracy and power consumption. In-house microelectro-mechanical (MEMS) devices, with a suspended nanowire structure, are manufactured to provide desired characteristics of small size, low power, and high sensitivity. Pattern recognition to combine the dimensionality reduction and the neural network is adopted to improve the selectivity of MEMS gas sensors. Moreover, potential deviations in sensing characteristics are calibrated through a proposed self-calibration zooming structure. Reconfigurable circuits for these key features are integrated into an adaptive readout integrated circuit which is fabricated in a 180-nm complementary metal-oxide semiconductor process. For its system-level verification, a wireless multichannel gas-sensor system prototype is implemented and experimentally verified to achieve 2.6 times efficiency improvement.

关键词： Monitoring Gas detectors Gases Micromechanical devices Sensor systems Pattern recognition Correlated double sampling (CDS) zooming gas-sensor system learning-based optimization prediction successive approximation register (SAR) analog-to-digital converters (ADC) self-calibration scheme

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A modified teaching-learning optimization algorithm for structural damage detection using a novel damage index based on modal flexibility and strain energy under environmental variations

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ENGINEERING WITH COMPUTERS 2022年第1期38卷 847-874页

作者： Ahmadi-Nedushan, Behrouz Fathnejat, Hamed Yazd Univ Dept Civil Engn Safayieh Yazd Iran

In this paper, a novel two-stage structural damage detection method using modal flexibility and strain energy-based index (MFSEBI) and modified teaching-learning-based optimization (MTLBO) algorithm is proposed. In the first stage, a novel damage index (MFSEBI) is proposed based on the combination of two structural modal properties including modal strain energy of elements and diagonal members of the structural flexibility matrix to identify the suspected damaged elements. The performance of this indicator is compared with the performance of an indicator derived from the modal strain energy. As the change in the structural modal properties is also affected by environmental variations, in this study, the effect of varying environmental conditions on the performance of the MFSEBI is also examined. In the second stage, the modal response of the structure is used and subsequently updated by MLTBO to estimate the damage extents of the suspected elements. At this stage, the MTLBO algorithm is proposed by enhancing the teaching and learning quality of the TLBO algorithm using new updating mechanisms of each learner position. The performance of the MTLBO and TLBO algorithms is compared in terms of the accuracy of the results and convergence rate. The results of three numerical examples indicate a better performance of the MFSEBI index in damage localization using a smaller number of mode shapes. These results also demonstrate the higher accuracy of the estimated damage extents and the faster convergence rate of the objective function using MTLBO, even with the consideration of measurement noise effects.

关键词： Modified teaching– learning-based optimization Two-stage damage detection method Damage index Flexibility matrix Modal strain energy Environmental variations

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A Survey of Recent Advances in optimization Methods for Wireless Communications

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第11期42卷 2992-3031页

作者： Liu, Ya-Feng Chang, Tsung-Hui Hong, Mingyi Wu, Zheyu So, Anthony Man-Cho Jorswieck, Eduard A. Yu, Wei Chinese Acad Sci Inst Computat Math & Sci Engn Comp Acad Math & Syst Sci State Key Lab Sci & Engn Comp Beijing 100190 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Tech Univ Carolo Wilhelmina Braunschweig Inst Commun Technol D-38106 Braunschweig Germany Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions.

关键词： Tutorials Array signal processing optimization methods Power control Resource management Wireless communication Scheduling Distributed computing Beamforming distributed optimization global optimization learning-based optimization integer optimization nonconvex nonsmooth optimization power control resource allocation scheduling sparse optimization wireless communications

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Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2023年第6期17卷 847-859页

作者： Samsonov, Vladimir Chrismarie, Enslin Koepken, Hans-Georg Baer, Schirin Luetticke, Daniel Meisen, Tobias Rhein Westfal TH Aachen Chair Informat Management Mech Engn WZL MQ IMA Aachen Germany Siemens AG Digital Ind Erlangen Germany Univ Wuppertal Chair Technol & Management Digital Transformat Wuppertal Germany

Computer Numerical Control (CNC) milling is a commonly used manufacturing process with a high level of automation. Nevertheless, setting up a new CNC milling process involves multiple development steps relying heavily on human expertise. In this work, we focus on positioning and orientation of the workpiece (WP) in the working space of a CNC milling machine and propose a deep learning approach to speed up this process significantly. The selection of the WP's setup depends on the chosen milling technological process, the geometry of the WP, and the capabilities of the considered CNC machining. It directly impacts the milling quality, machine wear, and overall energy consumption. Our approach relies on representation learning of the milling technological process with the subsequent use of reinforcement learning (RL) for the WP positioning and orientation. Solutions proposed by the RL agent are used as a warm start for simple hill-climbing heuristics, which boosts overall performance while keeping the overall number of search iterations low. The novelty of the developed approach is the ability to conduct the WP setup optimization covering both WP positioning and orientation while ensuring the axis collision avoidance, minimization of the axis traveled distances and improving the dynamic characteristics of the milling process with no input from human experts. Experiments show the potential of the proposed learning-based approach to generate almost comparably good WP setups order of magnitude faster than common metaheuristics, such as genetic algorithms (GA) and Particle Swarm Optimisation (PSA).

关键词： CNC Milling Deep representation learning learning-based optimization Process optimization Reinforcement learning Workpiece setup optimization

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Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 170卷

作者： Tian, Qingyun Lin, Yun Hui Wang, David Z. W. Yang, Kaidi Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore ASTAR Inst High Performance Comp IHPC 1 Fusionopolis Way16-16 Connexis Singapore 138632 Singapore Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore

Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.

关键词： Modular vehicle Headway and formation Rolling horizon control Convolutional neural network learning-based optimization

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General discriminative optimization for point set registration

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COMPUTERS & GRAPHICS-UK 2022年 102卷 521-532页

作者： Zhao, Yan Tang, Wen Feng, Jun Wan, Taoruan Xi, Long Bournemouth Univ Dept Creat Technol Poole BH12 5BB England Northwest Univ Dept Informat Sci & Technol Xian 710127 Peoples R China Univ Bradford Sch Informat Bradford BD7 1DP England

Point set registration has been actively studied in computer vision and graphics. optimization algorithms are at the core of solving registration problems. Traditional optimization approaches are mainly based on the gradient of objective functions. The derivation of objective functions makes it challenging to find optimal solutions for complex optimization models, especially for those applications where accuracy is critical. learning-based optimization is a novel approach to address this problem, which learns the gradient direction from datasets. However, many learning-based optimization algorithms learn gradient directions via a single feature extracted from the dataset, which will cause the updating direction to be vulnerable to perturbations around the data, thus falling into a bad stationary point. This paper proposes the General Discriminative optimization (GDO) method that updates a gradient path automatically through the trade-off among contributions of different features on updating gradients. We illustrate the benefits of GDO with tasks of 3D point set registrations and show that GDO outperforms the state-of-the-art registration methods in terms of accuracy and robustness to perturbations. Crown Copyright (C) 2021 Published by Elsevier Ltd. All rights reserved.

关键词： Point set registration Supervised learning learning-based optimization

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Plasma Generation optimization for Optimal Design of Reinforced Concrete Cantilever Retaining Wall Structures

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2022年第2期46卷 1177-1200页

作者： Kaveh, Ali Akbari, Hossein Hosseini, Seyed Milad Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

Optimal design of cantilever retaining wall structures is among the most commonly geotechnical structures supporting vertical or near-vertical slopes of soil. These structures are used in many locations such as highways, bridges, and railway infrastructures. This study aims to employ the recently developed population-based metaheuristic algorithm which is called Plasma Generation optimization (PGO) for the optimal design of reinforced concrete cantilever retaining wall structures. The design is based on the ACI 318-19 requirements, and optimizing construction cost is considered as the goal of the optimization problem. Two well-known theories including the Rankin and Coulomb theories are used in order to estimate the lateral earth pressure for the static loading condition. However, Mononobe-Okabe theory is utilized to determine this pressure for seismic loading condition. To investigate the efficiency of the PGO, two practical examples consisting of the cantilever retaining wall with shear key and cantilever retaining wall with sloped base are optimally designed based on the above-mentioned theories. Comparing the results acquired by the PGO with those of two well-known metaheuristics, namely Cuckoo Search and teaching-learning-based optimization, indicates the efficiency of PGO for the reinforced concrete cantilever retaining wall structures.

关键词： Cantilever retaining wall structures Optimal design Plasma Generation optimization Teaching– learning-based optimization Cuckoo Search

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