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Resonance algorithm: an intuitive algorithm to find all shortest paths between two nodes

COMPLEX & INTELLIGENT SYSTEMS 2023年第4期9卷 4159-4167页

作者： Liu, Yu Lin, Qiguang Hong, Binbin Peng, Yunru Hjerpe, Daniel Liu, Xiaofeng Beijing Normal Univ Int Acad Ctr Complex Syst Zhuhai 519087 Peoples R China Hohai Univ Coll IoT Engn Changzhou 213022 Peoples R China Shenzhen Univ Inst Microscale Optoelect Shenzhen 518060 Peoples R China Ericsson AB S-16483 Kista Sweden Jiangsu Key Lab Special Robot Technol Changzhou 213022 Peoples R China

The shortest path problem (SPP) is a classic problem and appears in a wide range of applications. Although a variety of algorithms already exist, new advances are still being made, mainly tuned for particular scenarios to have better performances. As a result, they become more and more technically complex and sophisticated. In this paper, we developed an intuitive and nature-inspired algorithm to compute all possible shortest paths between two nodes in a graph: Resonance algorithm (RA). It can handle any undirected, directed, or mixed graphs, irrespective of loops, unweighted or positively weighted edges, and can be implemented in a fully decentralized manner. Although the original motivation for RA is not the speed per se, in certain scenarios (when sophisticated matrix operations can be employed, and when the map is very large and all possible shortest paths are demanded), it out-competes Dijkstra's algorithm, which suggests that in those scenarios, RA could also be practically useful.

关键词： Dijkstra's algorithm Large-scale graph Nature-inspired algorithm decentralized algorithm Fermat's principle

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Connectivity maintenance for robotic swarms by distributed role switching algorithm

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ARTIFICIAL LIFE AND ROBOTICS 2022年第4期27卷 876-884页

作者： Kobayashi, Kazuho Higuchi, Takehiro Ueno, Seiya Yokohama Natl Univ Grad Sch Engn Sci Yokohama Kanagawa Japan Yokohama Natl Univ Fac Environm & Informat Sci Yokohama Kanagawa Japan

Swarm robotics requires a practical scheme to maintain operator supervision for the acceptability of systems' autonomy by humans. For this purpose, this paper proposes a distributed algorithm for continuous connectivity between robots and a base station to maintain the controllability and transparency of the swarm. This algorithm forms network topology among the swarm members and deploys repeaters to maintain the connection to the base station by a role switching scheme. Preliminary simulations have shown that the revised acute angle test reduced the cost of the network formation with the Gabriel graph topology. Through the simulated patrol missions, the proposed algorithm successfully maintained the continuous connectivity between the base station and the swarm members without significant inequality in the computational cost among swarm members. Furthermore, as the number of robots increases, the computational cost per robot does not increase significantly. These results indicate the distributed nature and scalability of the proposed algorithms.

关键词： Swarm robotics Role switching decentralized algorithm Connectivity maintenance

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Cooperative Localization Based on Efficient Covariance Intersection

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IEEE COMMUNICATIONS LETTERS 2019年第5期23卷 871-874页

作者： Lai, Jun Zhou, Yongbin Lin, Jinmao Cong, Yirui Yang, Jun Natl Univ Def Technol Coll Artificial Intelligence Changsha 410072 Hunan Peoples R China

This work proposes a geometric covariance intersection (CI)-based algorithm for efficiently solving the decentralized cooperative localization (CL) problem with multiple TOA measurements. Unlike existing algorithms that only process one measurement at a time, our proposed algorithm processes multiple measurements in a batch style, which improves the precision performance by considering the correlation among these measurements. An efficient way to get the optimal solution is given in our algorithm, which reduces the computational complexity to O(N logN). Furthermore, the sparsity of the optimal solution is proved, which enables a heuristic state buffering approach to reduce the usage of ranging link resources and improve measurement efficiency. The simulation results show that our algorithm outperforms the conventional methods.

关键词： Cooperative localization covariance intersection decentralized algorithm

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Consensus-Based decentralized Task Allocation for Multi-Agent Systems and Simultaneous Multi-Agent Tasks

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 12593-12600页

作者： Wang, Shengli Liu, Youjiang Qiu, Yongtao Zhou, Jie Tsinghua Univ Dept Engn Phys Beijing 100084 Peoples R China China Acad Engn Phys Inst Elect Engn Mianyang 621900 Peoples R China

This letter proposes a novel consensus-based timetable algorithm (CBTA) to solve the decentralized simultaneous multi-agent task allocation problem. Due to the limited capability of each agent, multiple agents may be required to perform a task simultaneously. A key challenge is how to meet the requirements and minimize the average start time of all tasks. The proposed CBTA aims to minimize the start time of each task to minimize the average start time of all tasks indirectly, it iterates between a timetable construction phase and a consensus phase. New tasks are included in the timetable of each agent by comparing the estimated start time of tasks placed by its own and other agents during the timetable construction phase. Then in the consensus phase, agents share their timetables with a communication network, and conflicts among their timetables are eliminated according to a consensus rule. Extensive simulation results show that the average start time of tasks of the proposed CBTA is nearly the same as the consensus-based bundle algorithm (CBBA) when performing single-agent tasks, and it is much less than the consensus-based grouping algorithm (CBGA) when performing multi-agent tasks with various communication network topologies.

关键词： decentralized algorithm multi-agent system task allocation and simultaneous multi-agent task

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Communication-Efficient decentralized Subspace Estimation

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 516-531页

作者： Jiao, Yuchen Gu, Yuantao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Machine learning techniques have been widely used in communication systems because of their superior performance. Among them, the classical machine learning algorithm Principal Component Analysis (PCA) aims to estimate the principal subspace of the received signals, and thus is also known as subspace estimation. It is often used in Direction of Arrival (DoA) tasks. Recently, the widespread deployment of networks has motivated researchers to solve the problem of subspace estimation in decentralized settings. An important concern in the design of decentralized algorithms is the communication complexity, because communication consumes much energy, while the sensors in the network are generally energy-limited. Specifically, communication complexity refers to the minimum amount of transmitted variables to obtain an estimate with an error smaller than epsilon. For existing algorithms, this complexity is O(log(2)(1/epsilon)). In this paper, we improve this complexity to be O(log(1/epsilon)) by designing a new algorithm named decentralized Subspace Estimation (DSE). Such improvement is achieved by using the gradient tracking technique. We theoretically analyze the influence of the network connectivity and the eigen-gap of the data on communication complexity. In addition, we use DSE to solve the DoA estimation problem. Experiments verify the effectiveness of the algorithm and the correctness of the theoretical result.

关键词： decentralized algorithm subspace estimation communication complexity average consensus wireless sensor networks

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decentralized dynamic functional network connectivity: State analysis in collaborative settings

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HUMAN BRAIN MAPPING 2020年第11期41卷 2909-2925页

作者： Baker, Bradley T. Damaraju, Eswar Silva, Rogers F. Plis, Sergey M. Calhoun, Vince D. Emory Univ Georgia State Univ Georgia Inst Technol Triinst Res Ctr Neuroimaging & Data Sci TReNDS Atlanta GA 30322 USA Mind Res Network Albuquerque NM USA Univ New Mexico Albuquerque NM 87131 USA

As neuroimaging data increase in complexity and related analytical problems follow suite, more researchers are drawn to collaborative frameworks that leverage data sets from multiple data-collection sites to balance out the complexity with an increased sample size. Although centralized data-collection approaches have dominated the collaborative scene, a number of decentralized approaches-those that avoid gathering data at a shared central store-have grown in popularity. We expect the prevalence of decentralized approaches to continue as privacy risks and communication overhead become increasingly important for researchers. In this article, we develop, implement and evaluate a decentralized version of one such widely used tool: dynamic functional network connectivity. Our resulting algorithm, decentralized dynamic functional network connectivity (ddFNC), synthesizes a new, decentralized group independent component analysis algorithm (dgICA) with algorithms for decentralized k-means clustering. We compare both individual decentralized components and the full resulting decentralized analysis pipeline against centralized counterparts on the same data, and show that both provide comparable performance. Additionally, we perform several experiments which evaluate the communication overhead and convergence behavior of various decentralization strategies and decentralized clustering algorithms. Our analysis indicates that ddFNC is a fine candidate for facilitating decentralized collaboration between neuroimaging researchers, and stands ready for the inclusion of privacy-enabling modifications, such as differential privacy.

关键词： brain imaging data sharing decentralized decentralized algorithm dFNC dynamic functional network connectivity

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Modeling biological memory network by an autonomous and adaptive multi-agent system

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BRAIN INFORMATICS 2024年第1期11卷 23页

作者： Wei, Hui Feng, Chenyue Li, Fushun Fudan Univ Sch Comp Sci Lab Algorithms Cognit Models 2005 Songhu Rd Shanghai 200438 Peoples R China

At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships description of complex relationships and structures, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a multi-agent system (MAS) model based on the graph theory, each agent equipped with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information memory, modeling and simulation of the brain's memory process. This decentralized approach transforms memory storage into the management of MAS paths, with each agent utilizing localized information for the dynamic formation and modification of these paths, different path refers to different memory instance. The model's unique memory algorithm avoids a global view, instead relying on neighborhood-based interactions to enhance resource utilization. Emulating neuron electrophysiology, each agent's adaptive learning behavior is represented through a microcircuit centered around a variable resistor. Using principles of Ohm's and Kirchhoff's laws, we validated the model's efficacy in memorizing and retrieving data through computer simulations. This approach offers a plausible neurobiological explanation for memory realization and validates the memory trace theory at a system level.

关键词： Memory modeling MAS Memory simulation decentralized algorithm

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Multi-AGV Path Planning for Indoor Factory by Using Prioritized Planning and Improved Ant algorithm

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JOURNAL OF ENGINEERING AND TECHNOLOGICAL SCIENCES 2018年第4期50卷 534-547页

作者： Zhang, Yi Wang, Fuli Fu, Fukang Su, Zuqiang Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

Multiple automated guided vehicle (multi-AGV) path planning in manufacturing workshops has always been technically difficult for industrial applications. This paper presents a multi-AGV path planning method based on prioritized planning and improved ant colony algorithms. Firstly, in dealing with the problem of path coordination between AGVs, an improved priority algorithm is introduced, where priority is assigned based on the remaining battery charge of the AGVs, which improves the power usage efficiency of the AGVs. Secondly, an improved ant colony algorithm (IAC) is proposed to calculate the optimal path for the AGVs. In the algorithm, a random amount of pheromone is distributed in the map and the amount of pheromone is updated according to a fitness value. As a result, the computational efficiency of the ant colony algorithm is improved. Moreover, a mutation operation is introduced to mutate the amount of pheromone in randomly selected locations of the map, by which the problem of local optimum is well overcome. Simulation results and a comparative analysis showed the validity of the proposed method.

关键词： ant algorithm collision avoidance decentralized algorithm path planning

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Phase Retrieval With Learning Unfolded Expectation Consistent Signal Recovery algorithm

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 780-784页

作者： Wang, Chang-Jen Wen, Chao-Kai Tsai, Shang-Ho Jin, Shi Natl Chiao Tung Univ Inst Elect & Control Engn Hsinchu 300 Taiwan Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 804 Taiwan Natl Chiao Tung Univ Dept Elect Engn Hsinchu 300 Taiwan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accuracy, speed, and robustness than numerous existing methods. decentralized GEC-SR (deGEC-SR) addresses the scalability issue in high-resolution images. However, the convergence speed and stability of these algorithms heavily rely on the settings of several handcrafted tuning factors with inefficient turning process. In this work, we propose deGEC-SR-Net by unfolding the iterative deGEC-SR algorithm into a learning network architecture with trainable parameters. The parameters of deGEC-SR-Net are determined by data-driven training. Numerical results show that deGEC-SR-Net provides substantially faster convergence than deGEC-SR and exhibits superior robustness to noise and prior mis-specifications.

关键词： Phase retrieval deep neural network unfolding decentralized algorithm

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Energy Efficient Mobile Computation Offloading through Workload Migration

Energy Efficient Mobile Computation Offloading through Workl...

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2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Gao, Chengxi Lee, Victor C. S. City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509018932

The popularity of mobile cloud computing has provisioned a new paradigm for mobile devices to offload their computation to remote mobile cloud data center for task execution. However, the mobile cloud data center may suffer from dramatic energy consumption. To solve this problem, we propose to migrate the workload from remote cloud data center to nearby cloudlets, so as to relieve the pressure of cloud data center and save the energy consumption. Specifically, we propose a lightweight and decentralized algorithm based on the Alternating Direction Method of Multipliers (ADMM) algorithm to construct the migration scheme. Simulations show that our algorithm can fast converge in tens of iterations, and decrease the overall energy consumption.

关键词： Energy efficiency Mobile computation offloading Workload migration decentralized algorithm Cloudlets

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