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A Delaunay triangulation based dynamic pricing approach to profit-driven crowdsourced package delivery

IET INTELLIGENT TRANSPORT SYSTEMS 2024年第6期18卷 984-1003页

作者： Zhou, Zhifeng Chen, Rong Deng, Wu Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin Peoples R China Dalian Maritime Univ Coll Informat Sci & Technol 1 Linghai Rd Dalian 116026 Liaoning Peoples R China

Crowdsourced package delivery (CPD) has gained great interest from the logistics industry and academe due to its signficant economic and environmental impact. A number of progresses have been reported;however, dynamic pricing, as a special and significant part of the uncertain CPD markets, is far less explored. To address the problem, a novel Delaunay Triangulation (DT) based pricing strategy is proposed for dealing with the imbalanced demand and supply in local markets with the aim to maximizing the profit of the CPD platform. To cater to drivers' wishes further, the hyperbolic temporal discounting function is applied to estimate their psychological rewards in order to increase the possibility of package acceptance. A three-stage framework consisting of the DT-based pricing, the matching pruning and the package assigning algorithm is proposed, to figure out the best-possible package-driver matching. With the dataset generated by Brinkhoff road network generator in the city of Jinan and Luoyang, China, a series of experiments is conducted to evaluate the proposed approach against several representative baseline approaches. Experimental results show that the approach significantly outperforms the other approaches in terms of effectiveness and efficiency. Crowdsourced package delivery, dynamic pricing, Delaunay Triangulation, temporal discounting, package assigning

关键词： crowdsourcing greedy algorithms optimization and uncertainty

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Skeleton Extraction and greedy-Algorithm-Based Path Planning and its Application in UAV Trajectory Tracking

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第6期58卷 4953-4964页

作者： Chang, Jianfang Dong, Na Li, Donghui Ip, Wai Hung Yung, Kai Leung Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

Space research is of great significance to increasingly decentralized and distributed space systems, and path planning in space systems has become a research hotspot for maintaining their safety, security, and reliability. To explore the passable path connecting the starting point and the target point, and optimize a smooth trajectory that can be tracked by unmanned aerial vehicles (UAVs) in 3-D space, a skeleton-extraction- and greedy-algorithm-based path planning has been proposed to guide the flight of UAVs. First, the rapidly exploring random tree (RRT) has been introduced for path search. To speed up the path search process, the spatial skeleton extraction method has been introduced to calculate the skeleton of free space The greedy algorithm has been utilized to increase the RRT expansion and reduce unnecessary bends in the path. The skeleton extraction and greedy-algorithm-based Lazy RRT and RRT-Connect have been proposed to build compared experiments. Second, the minimum snap has been applied to generate a smooth flight trajectory, and the flight time is allocated according to the distance between the waypoints. Third, the UAV Simulink model has been established, and the spatial position of the optimized trajectory is tracked. The experimental results prove that the skeleton extraction can significantly speed up the search process, and greedy algorithm can shorten the path length effectively. The minimum snap combined with the time allocation strategy can produce a smooth and feasible path. The UAV Simulink model also proves that the classic proportion-differentiation controller can accurately track the generated trajectory. The greedy algorithm and skeleton extraction reduce the average path length of RRT by 9.780 and 8.251%, respectively. The greedy algorithm and skeleton extraction reduce the average path length of RRT, Lazy RRT, and RRT-Connect by about 10-13%. The greedy algorithm and skeleton extraction reduce the time consumption of RRT by 62.781 and

关键词： Skeleton Planning greedy algorithms Path planning Trajectory tracking Trajectory optimization Autonomous aerial vehicles greedy algorithm rapidly exploring random tree (RRT) Skeleton extraction space research tracking trajectory optimization

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A Background M-Channel Time-Interleaved ADC Calibration for Multilevel Modulations Based on the Probability Density Function Difference and a greedy Algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 4140-4155页

作者： Tavares, Yang A. Kim, Seunghyun Lee, Minjae Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

This paper presents the first time-interleaved analog-to-digital background calibration targeted for multilevel modulations that can calibrate offset, gain, time skew and bandwidth mismatches (to the author's knowledge). The mismatch errors are estimated based on the interleaving channels histograms. Moreover, the proposed calibration can be adapted to any interleaving factor with a multiplication-less estimation logic based on a greedy algorithm. Numerical simulations show satisfactory performance improvements for a 2-level, a 4-level and an 8-level symbol transmission, along with 4-channel and 32-channel TI-ADC interleaving orders. Furthermore, a new SNDR formula is derived considering all the TI-ADC mismatch distortions that can be compared with the obtained results. The measurement results show the proposed calibration working under a binary PRBS31 pattern with both a 3.6 GS/s and 7.2 GS/s signaling rates for a 3.6 GS/s 2-channel TI-ADC experimental board.

关键词： Calibration Probability density function Symbols Histograms Bandwidth Modulation Hardware Analog-digital conversion calibration greedy algorithms

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Parameter Estimation of Communication Radar Fusion System Based on Orthogonal Frequency Division Multiplexing Technology

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IEEE ACCESS 2024年 12卷 39662-39677页

作者： Hu, Xiaoguang Zhang, Rui Cui, Junpeng Tianjin Sino German Univ Appl Sci Software & Commun Sch Tianjin 300350 Peoples R China

The development of communication and radar fusion systems is critical to enhancing sensor network security. However, in spectrum resource environments, the technology for processing fusion signals may face limitations due to multi-user access and interference issues. Traditional signal processing algorithms may also be influenced by waveform information. As a result, its implementation is limited in low-noise-ratio settings. Based on the advantages of multiple-input multiple-output technology in the field of communication and radar, in order to better ensure the non-interference between the communication and radar systems, the study introduces the orthogonal theory. It proposes a target detection and parameter estimation method based on orthogonal frequency division multiplexing (OFDM) technology in the signal modulation domain. It utilizes sparse tensor theory to obtain low rank features of signals, transform the dimensions of target parameter problems, and solve tensor rank 1 approximation problems. This can better improve the performance of target parameter estimation. The simulation analysis of the proposed method shows that the average error values of the improved decomposition method in Doppler frequency shift estimation and angle estimation are close to 10-2 and 10-1 respectively. The system runs for less than 5 seconds, and its average target detection accuracy exceeds 96%. The proposed orthogonal frequency division multiplexing technique can provide technical support for sensor networks in wireless communication and IOT networks, efficiently reducing computational complexity and interference among users. This can serve as a valuable reference for enhancing the security of communication radar fusion systems.

关键词： Radar OFDM Tensors Parameter estimation Doppler radar Radar antennas greedy algorithms Communication systems Object detection Signal processing algorithms Interference Image fusion Orthogonal frequency division multiplexing technology parameter estimation sparse tensor theory greedy CP decomposition target scenario

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Advice complexity of adaptive priority algorithms

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THEORETICAL COMPUTER SCIENCE 2024年 984卷

作者： Boyar, Joan Larsen, Kim S. Pankratov, Denis Univ Southern Denmark Dept Math & Comp Sci Campusvej 55 DK-5230 Odense M Denmark Concordia Univ Dept Comp Sci & Software Engn 2155 Guy St Montreal PQ H3H 2L9 Canada

The priority model was introduced to capture "greedy-like" algorithms. Motivated by the success of advice complexity in the area of online algorithms, the fixed priority model was extended to include advice, and a reduction-based framework was developed for proving lower bounds on the amount of advice required to achieve certain approximation ratios in this rather powerful model. To capture most of the algorithms that are considered greedy-like, the even stronger model of adaptive priority algorithms is needed. We extend the adaptive priority model to include advice. We modify the reduction-based framework from the fixed priority case to work with the more powerful adaptive priority algorithms, simplifying the proof of correctness and strengthening all previous lower bounds by a factor of two in the process. As evidence that adding advice to adaptive priority algorithms extends both adaptive priority algorithms and online algorithms with advice, we present a purely combinatorial adaptive priority algorithm with advice for Minimum Vertex Cover on triangle-free graphs of maximum degree three. Our algorithm achieves optimality and uses at most 7n/22 bits of advice. No adaptive priority algorithm without advice can achieve optimality without advice, and we prove that an online algorithm with advice needs more than 7n/22 bits of advice to reach optimality. We show connections between exact algorithms and priority algorithms with advice. The branching in branch-and-reduce algorithms can be seen as trying all possible advice strings, and all priority algorithms with advice that achieve optimality define corresponding exact algorithms, priority exact algorithms. Lower bounds on advice-based adaptive algorithms imply lower bounds on running times of exact algorithms designed in this way.

关键词： greedy algorithms Priority algorithms Adaptive priority algorithms Exact algorithms Online algorithms Advice complexity

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Joint User and Beam Selection in Millimeter Wave Networks

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 881-896页

作者： Singh, Santosh Kumar Sahu, Satyabrata Thawait, Ayushi Chaporkar, Prasanna Kasbekar, Gaurav S. Indian Inst Technol IIT Bombay Dept Elect Engn Mumbai 400076 India Samsung Semicond Radio Firmware Connect Solut Bengaluru 560048 India Minist Civil Aviat Airports Author India AAI New Delhi 110003 India Govt India New Delhi 110003 India

We investigate the problem of maximizing the sum of the weighted rates of user equipment (UE) by finding a UE and a beam for each access point (AP) for concurrent transmission in millimeter wave (mmWave) networks. We prove that this problem is NP-complete. We propose two algorithms- Markov Chain Monte Carlo (MCMC) based and local interaction game (LIG) based UE and beam selection- and prove that both of them asymptotically achieve the optimal solution. Also, we propose two fast greedy heuristics-NGUB1 and NGUB2-for UE and beam selection. Using detailed simulations, we demonstrate that the performance of our proposed greedy heuristics is close to that of the asymptotically optimal algorithms and superior to that of the most relevant algorithms proposed in prior work.

关键词： User selection beam selection millimeter wave networks NP-complete problems markov chain monte carlo (MCMC) based algorithm local interaction game (LIG) based algorithm greedy algorithms User selection beam selection millimeter wave networks NP-complete problems markov chain monte carlo (MCMC) based algorithm local interaction game (LIG) based algorithm greedy algorithms

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Community-Based Memetic Algorithm for Influence Maximization in Large-Scale Networks

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IEEE ACCESS 2025年 13卷 72754-72768页

作者： Roy, Mithun Pan, Indrajit Siliguri Inst Technol Siliguri 734009 West Bengal India RCC Inst Informat Technol Kolkata 700015 West Bengal India

Effective information diffusion across large-scale network is key for influence maximization. Recent research has shown a significant surge in interest in modeling, performance estimation, and seed identification across various networked systems. Moreover, a simulation of useful interactions among many significant groups within networks was developed to simulate real-world marketing and spreading information more accurately. A good diffusion model identifies the minimum number of effective seeds capable of achieving maximum diffusion effects across the network. Limited focus has been placed on measuring the strength of seeds in competitive spreading situations. There is a research gap in determining effective strategy for this purpose. This study proposes a memetic algorithm based on a community for large-scale social networks. The proposed algorithm optimizes the influence spread by identifying the most influential nodes among the communities, depending on their inter- or intra-community propagation dynamics. This algorithm combines the concept of genetic algorithm with a reachability-based local search method to accelerate the convergence process. This approach offers a robust method for maximizing the influence of network structure and interactions. An experimental evaluation on real-world social network datasets shows the performance superiority of this community-based memetic algorithm (CBMA-IM) over existing algorithms.

关键词： Social networking (online) Memetics Heuristic algorithms Genetic algorithms Clustering algorithms Optimization greedy algorithms Evolutionary computation Integrated circuit modeling Diffusion models Community structure influence maximization local search method memetic algorithm social structural strength

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On the Benefits of Progressively Increasing Sampling Sizes in Stochastic greedy Weak Submodular Maximization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3978-3992页

作者： Hashemi, Abolfazl Vikalo, Haris de Veciana, Gustavo Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

Many problems in signal processing and machine learning can be formalized as weak submodular optimization tasks. For such problems, a simple greedy algorithm (greedy) is guaranteed to find a solution achieving the objective with a value no worse than 1 - e(-1/c) of the optimal, where c is the multiplicative weak-submodularity constant. Due to the high cast of querying large-scale systems, the complexity of greedy becomes prohibitive in contemporary applications. In this work, we study the tradeoff between performance and complexity when one resorts to random sampling strategies to reduce the query complexity of greedy. Specifically, we quantify the effect of uniform sampling strategies on greedy's performance through two metrics: (i) asymptotic probability of identifying an optimal subset, and (ii) suboptimality with respect to the optimal solution. The latter implies that uniform sampling strategies with a fixed sampling size achieve a non-trivial approximation factor;however, we show that with overwhelming probability, these methods fail to find the optimal subset. Our analysis shows that the failure of uniform sampling strategies with fixed sample size can be circumvented by successively increasing the size of the search space. Building upon this insight, we propose a simple progressive stochastic greedy algorithm and study its approximation guarantees. Moreover, we demonstrate effectiveness of the proposed method in dimensionality reduction applications and feature selection tasks for clustering and object tracking.

关键词： Weak submodular optimization greedy algorithms randomized algorithms subset selection

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Budgeted Sequence Submodular Maximization With Uniform and Non-Uniform Costs

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 503-518页

作者： Chen, Xuefeng Feng, Liang Cao, Xin Zeng, Yifeng Hou, Yaqing Zhang, Zhi Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST England Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

The problem of picking a sequence of items that maximizes a given submodular function over the item sequence exists in many practical applications. Existing studies on this problem only consider uniform costs on items. However, it is more general that items have non-uniform costs. Taking this cue, in this article, we study the problem of budgeted sequence submodular maximization (BSSM), which considers both uniform and non-uniform item costs in the sequence selection. This problem widely appears in many applications such as movie recommendation and course learning. For BSSM with uniform costs, we first prove a new approximation ratio for an existing algorithm OMEGA, and then propose an anytime randomized iterative algorithm POBM which converges faster than a state-of-the-art method POSEQSEL in obtaining approximate solutions. We also develop some optimization techniques for improving the efficiency of POBM. For BSSM with non-uniform costs, we design a greedy algorithm GBM with a guaranteed error bound, and prove that POBM can find approximate solutions in a reasonable time. The results of the experimental study on both synthetic and real datasets demonstrate the excellent performance of our algorithms.

关键词： Costs Optimization Motion pictures greedy algorithms Iterative methods Computational intelligence Approximation algorithms Sequence submodular maximization submodular optimization evolutionary optimization theoretical analysis experimental study

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Product kernels are efficient and flexible tools for high-dimensional scattered data interpolation

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ADVANCES IN COMPUTATIONAL MATHEMATICS 2025年第2期51卷 1-27页

作者： Albrecht, Kristof Entzian, Juliane Iske, Armin Univ Hamburg Dept Math Hamburg Germany

This work concerns the construction and characterization of product kernels for multivariate approximation from a finite set of discrete samples. To this end, we consider composing different component kernels, each acting on a low-dimensional Euclidean space. Due to Aronszajn (Trans. Am. Math. Soc. 68, 337-404 1950), the product of positive semi-definite kernel functions is again positive semi-definite, where, moreover, the corresponding native space is a particular instance of a tensor product, referred to as Hilbert tensor product. We first analyze the general problem of multivariate interpolation by product kernels. Then, we further investigate the tensor product structure, in particular for grid-like samples. We use this case to show that the product of positive definite kernel functions is again positive definite. Moreover, we develop an efficient computation scheme for the well-known Newton basis. Supporting numerical examples show the good performance of product kernels, especially for their flexibility.

关键词： Kernel-based approximation Product kernels greedy algorithms

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