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greedy Selection for Heterogeneous Sensors

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1394-1409页

作者： Majumder, Kaushani Pillai, Sibi Raj B. Mulleti, Satish Indian Inst Technol Dept Elect Engn Mumbai 400076 India

Simultaneous operation of all sensors in a large-scale sensor network is power-consuming and computationally expensive. Hence, it is desirable to select fewer sensors. A greedy algorithm is widely used for sensor selection in homogeneous networks with a theoretical worst-case performance of ( 1-1/e) approximate to 63% of the optimal performance when optimizing submodular metrics. For heterogeneous sensor networks (HSNs) comprising multiple sets of sensors, most of the existing sensor selection methods optimize the performance constrained by a budget on the total value of the selected sensors. However, in many applications, the number of sensors to select from each set is known apriori and solutions are not well-explored. For this problem, we propose a joint greedy heterogeneous sensor selection algorithm. Theoretically, we show that the worst-case performance of the proposed algorithm is bounded to 50$% of the optimum for submodular cost metrics. In the special case of HSNs with two sensor networks, the performance guarantee can be improved to 63% when the number of sensors to select from one set is much smaller than the other. To validate our results experimentally, we propose a submodular metric based on the frame potential measure that considers both the correlation among the sensor measurements and their heterogeneity. We prove theoretical bounds for the mean squared error of the solution when this performance metric is used. We validate our results through simulation experiments considering both linear and non-linear measurement models corrupted by additive noise and quantization errors. Our experiments show that the proposed algorithm results in 4- 10 dB lower error than existing methods.

关键词： Sensors Sensor phenomena and characterization Costs Measurement Vectors Signal processing algorithms Noise measurement Intelligent sensors greedy algorithms Accuracy Sensor selection heterogeneous sensor networks submodular maximization greedy algorithm frame potential

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Thermal stability enhancement of erythromycin esterase C from Enterobacter hormaechei through a combinatorial greedy accumulated strategy

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2025年第Pt 2期305卷 141207页

作者： Zhang, Yulei Huang, Zhaolin Zhang, Wenli Ni, Dawei Xu, Wei Guang, Cuie Mu, Wanmeng Jiangnan Univ State Key Lab Food Sci & Resources Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Food Sci & Technol State Key Lab Food Sci & Resources Wuxi 214122 Jiangsu Peoples R China

The presence of macrolides in food and the environment poses a substantial threat to the sustainable development of natural ecosystems and presents risks to human health. Previous research has demonstrated that microbial esterases can degrade erythromycin (Ery), roxithromycin (Rox), and clarithromycin (Clr), rendering them candidates for bioremediation of macrolide residues. However, the efficacy of esterases is limited by insufficient thermal stability, restricting their widespread application. To address this challenge, this study employed a combinatorial strategy integrating computational molecular dynamics (MD) simulations, server-assisted virtual mutation prediction, and a greedy algorithm for protein engineering to enhance the thermal stability of esterase C from Enterobacter hormaechei (Ehno-EreC). After four rounds of screening, we successfully obtained a four-point mutant, M4 (D245P/S141A/D154I/T292W), which exhibited significantly improved thermal stability compared to the wild-type enzyme. Notably, the M4 mutant not only extended the enzyme's half-life at 40 degrees C from 7 to 1095 min, but also increased its degradation activity against Ery, Rox, and Clr. Structural analyses revealed enhanced rigidity in the loop between the major and minor lobes and the strengthened hydrophobic interactions post-mutation, contributing to the enzyme's structural stability. This finding was consistent with the observed reduction in correlated and anti-correlated motions in the MD trajectory.

关键词： Macrolides residue Esterase Thermostability greedy algorithm Modification Application

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greedy Sidon sets for linear forms

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JOURNAL OF NUMBER THEORY 2025年 266卷 225-248页

作者： Cheng, Yin Choi CUNY Grad Ctr Dept Math New York NY 10017 USA

The greedy Sidon set, also known as the Mian-Chowla sequence, is the lexicographically first set in N that does not contain x(1), x(2), y(1), y(2) with x(1 )+ x(2) = y(1 )+ y(2). Its growth and structure have remained enigmatic for 80 years. In this work, we study a generalization from the form x(1)+x(2) to arbitrary linear forms c(1)x(1 )+ & mldr;+ c(h)x(h);these are called Sidon sets for linear forms. We explicitly describe the elements of the greedy Sidon sets for linear forms when c(i) = n(i-1) for some n >= 2, and also when h = 2, c(1 )= 2, c(2 )>= 4, the "structured" domain. We also contrast the "enigmatic" domain when h = 2, c(1) = 2, c(2 )= 3 with the "structured" domain, and give upper bounds on the growth rates in both cases. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Sidon sets Linear forms greedy algorithm Mian-Chowla

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Optimized path planning for underground coal mine filling: an greedy adaptive directional bidirectional rapidly-exploring random tree

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 026205-026205页

作者： Zhang, Zihang Liu, Yang Bo, Lei Yang, Shangqing Wang, Yanwen China Univ Min & Technol Sch Artificial Intelligence Beijing 100083 Peoples R China China Univ Min & Technol Sch Mech & Elect Engn Beijing 100083 Peoples R China

The technology of mining filling is of great significance in improving coal recovery rates, protecting the environment, and conserving land resources. The current efficiency of filling is constrained by single-method approaches. To address this issue, this study develops a path planning model based on underground fill space data, which comprehensively considers fill path length and material volume using a goal programming method, and designs corresponding constraints and adaptive weights. To further optimize search efficiency, an adaptive directional bidirectional rapidly-exploring random tree (AD-BIRRT) algorithm is proposed. This algorithm can intelligently adjust the optimal exploration direction based on current demand and state, significantly enhancing search efficiency and accuracy through the establishment of a dual-tree structure. Additionally, a novel greedy strategy is introduced to resolve path smoothing and redundancy issues. To verify the rationality and effectiveness of the proposed method, comparative tests were conducted in test scenarios, fill scenarios, and on experimental platforms against BIRRT, BIRRT*, Genetic algorithm, and artificial potential field algorithms. The results indicate that the proposed greedy AD-BIRRT algorithm exhibits significant advantages in terms of computation time, path quality, and material accumulation. This algorithm effectively enhances the efficiency and quality of the filling process.

关键词： solid filling path planning optimization algorithm BIRRT greedy algorithm

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A hybrid greedy indicator- and Pareto-based many-objective evolutionary algorithm

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APPLIED INTELLIGENCE 2021年第7期51卷 4330-4352页

作者： de Oliveira, Matheus Carvalho Delgado, Myriam Regattieri Britto, Andre Univ Fed Sergipe Postgrad Program Comp Sci Sao Cristovao Brazil Univ Tecnol Fed Parana Acad Dept Informat Curitiba Parana Brazil Univ Fed Sergipe Dept Computat Sao Cristovao Brazil

As most of Multi-Objective Evolutionary algorithms (MOEAs) scale quite poorly when the number of objective functions increases, new strategies have been proposed to face this limitation. Considered one of the most well-succeeded examples of such new strategies, the third version of Non-dominated Sorting Genetic algorithm (NSGA-III) uses a set of reference points placed on a normalized hyperplane to solve Many Objective Optimization Problems (MaOPs). Despite the good results of NSGA-III, the shape of the hypersurface can influence the algorithm's performance and it has not been deeply explored in the recent literature. This work aims therefore at proposing a new hybrid algorithm and investigating nonlinear transformations applied to the NSGA-III hyperplane. We categorize our proposed approach as a hybrid MOEA (Indicator- and Pareto-based), and analyze the influence of the modified set of reference points on the proposed algorithm while solving MaOPs. The paper presents as its main contributions: (i) the proposal of a greedy indicator-based variant of NSGA-III that uses math transformations on a subset of chosen reference points;(ii) the use of Vector Guided Adaptation procedure to modify original NSGA-III hyperplane at every few iterations. A set of experiments addressing several benchmark problems is performed to evaluate the performance of the original and adapted NSGA-III versions, and also to compare them with two well known MOEAs, observing both, convergence and diversity of the final Pareto fronts. Based on the analysis of statistical tests, we conclude that the proposed approach is competitive and can provide an interesting alternative to the original NSGA-III conception.

关键词： Many-objective optimization Reference points NSGA-III greedy algorithm

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Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems

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ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS 2025年第1期59卷 291-330页

作者： Kleikamp, Hendrik Lazar, Martin Molinari, Cesare Univ Munster Inst Anal & Numer Math Munster Einsteinstr 62 D-48149 Munster Germany Univ Dubrovnik Dept Elect Engn & Comp Cira Carica 4 Dubrovnik 20000 Croatia Univ Genoa Dipartimento Matemat Via Dodecaneso 35 I-16146 Genoa Italy

We consider parametrized linear-quadratic optimal control problems and provide their online-efficient solutions by combining greedy reduced basis methods and machine learning algorithms. To this end, we first extend the greedy control algorithm, which builds a reduced basis for the manifold of optimal final time adjoint states, to the setting where the objective functional consists of a penalty term measuring the deviation from a desired state and a term describing the control energy. Afterwards, we apply machine learning surrogates to accelerate the online evaluation of the reduced model. The error estimates proven for the greedy procedure are further transferred to the machine learning models and thus allow for efficient a posteriori error certification. We discuss the computational costs of all considered methods in detail and show by means of two numerical examples the tremendous potential of the proposed methodology.

关键词： Parametrized optimal control greedy algorithm machine learning deep neural networks kernel methods error estimation

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Average-Case Analysis of greedy Matching for Large-Scale D2D Resource Sharing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 3707-3721页

作者： Gao, Shuqin Courcoubetis, Costas A. Duan, Lingjie Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China

Given the proximity of many wireless users and their diversity in consuming local resources (e.g., data-plans, computation and energy resources), device-to-device (D2D) resource sharing is a promising approach towards realizing a sharing economy. This paper adopts an easy-to-implement greedy matching algorithm with distributed fashion and only sub-linear $O(\log n)$O(logn) parallel complexity (in user number $n$n) for large-scale D2D sharing. Practical cases indicate that the greedy matching's average performance is far better than the worst-case approximation ratio 50% as compared to the optimum. However, there is no rigorous average-case analysis in the literature to back up such encouraging findings and this paper is the first to present such analysis for multiple representative classes of graphs. For 1D linear networks, we prove that our greedy algorithm performs better than 86.5% of the optimum. For 2D grids, though dynamic programming cannot be directly applied, we still prove this average performance ratio to be above 76%. For the more challenging Erdos-Renyi random graphs, we equivalently reduce to the asymptotic analysis of random trees and successfully prove a ratio up to 79%. Finally, we conduct experiments using real data to simulate realistic D2D networks, and show that our analytical performance measure approximates well practical cases.

关键词： Device-to-device communication Resource management Complexity theory Wireless communication Approximation algorithms Videos Performance evaluation Average-case analysis greedy algorithm large-scale resource sharing weighted matching

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greedy is Good:Constrained Non-submodular Function Maximization via Weak Submodularity

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Journal of the Operations Research Society of China 2024年第3期12卷 627-648页

作者： Ma-Jun Shi Wei Wang School of Statistics Xi’an University of Finance and EconomicsXi’an710100ShaanxiChina School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’an710049ShaanxiChina

The widely used greedy algorithm has been recently shown to achieve near-optimal theoretical guarantees for the problems of constrained monotone non-submodular function maximization,with competitive performances in *** this paper,we investigate the problems of maximizing monotone non-submodular set functions under three classes of independent system constraints,including p-matroid intersection constraints,p-extendible system constraints and p-system *** prove that the greedy algorithm yields an approximation ratio ofγ/p+γfor the former two problems,andξγ/p+ξγfor the last problem,which further has been improved toγ/p+γ,whereγ,ξdenote the submodularity ratio and the diminishing returns ratio of set function *** addition,we also show that the greedy guarantees have a further refinement of for all the problems mentioned above,whereαis the generalized curvatureξ/p+αγof set ***,we show that our greedy algorithm does yield competitive practical performances using a variety of experiments on synthetic data.

关键词： Non-submodular function p-matroid intersection p-extendible system p-system greedy algorithm

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OptiGSM: greedy-Based Load Balancing With Minimum Switch Migrations in Software-Defined Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第2期21卷 2200-2210页

作者： Prajapati, Upendra Chatterjee, Bijoy Chand Banerjee, Amit South Asian Univ Dept Comp Sci New Delhi 110068 India

Software-defined networks (SDNs) are an emerging technology to improve the scalability and network performance of data-intensive applications with high-volume traffic. Load balancing among controllers with several switch migrations is a major obstacle in a multi-controller SDN, degrading the overall system's performance. To resolve the above issue, this paper proposes OptiGSM, an optimum greedy-based switch migration approach for load balancing among the controllers. OptiGSM starts the switch migration process from an overloaded controller to an underloaded controller if the mean-square deviation of a load of each controller from the average load of all controllers is greater than a pre-defined threshold. A controller is overloaded if its load is greater than the average load of all controllers and vice-versa. We introduce an algorithm for load balancing and prove that it achieves load balancing using the minimum number of switch migrations. Next, we analyze the time and space complexities of the algorithm. Furthermore, we evaluate OptiGSM using simulation and experimental studies. Numerical results indicate that OptiGSM outperforms conventional approaches in terms of the number of switch migrations, response time, throughput, and delay.

关键词： Switches Control systems Load management Throughput Process control Time factors Delays Software-defined networks switch migration load balance multi-controller greedy algorithm OpenFlow zodiac-FX Ryu controller

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Automated fault diagnosis of rotating machinery using sub domain greedy Network Architecture search

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Lai, Yanzuo Shao, Haidong Zheng, Xinyu Cai, Baoping Liu, Bin Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Peoples R China Univ Strathclyde Dept Management Sci Glasgow G1 1XQ Scotland

Network Architecture Search (NAS) automates hyperparameter adjustments in deep learning models, offering a potent solution for building intelligent fault diagnosis models. Despite its potential, NAS faces challenges in this application. The primary issues include the excessive time required when searching across the full data domain and the inefficacy of the common random search strategy in yielding high-performance sub networks. This study addresses these challenges through several key initiatives: It defines a specific library of hyperparameters tailored for intelligent fault diagnosis tasks;constructs sub data domain model clusters to reduce the computational costs associated with NAS;and introduces a hyperparameter search strategy leveraging a greedy algorithm, which enhances the search efficiency for optimal diagnostic sub network and minimizes its prediction errors. When applied to datasets involving gear and bearing faults, the proposed method demonstrates a reduction in both the time cost of searches and the prediction errors of sub networks, compared to traditional Network Architecture Search. Moreover, it outperforms manually tuned deep learning models by simultaneously optimizing multiple sets of hyperparameter and automatically generating higher accuracy diagnostic sub networks.

关键词： Automated fault diagnosis Rotating machinery Sub data domain greedy algorithm Network Architecture search

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