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A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing

IEEE TRANSACTIONS ON COMPUTERS 2022年第7期71卷 1612-1625页

作者： Liao, Xiaofei Zhao, Jin Zhang, Yu He, Bingsheng He, Ligang Jin, Hai Gu, Lin Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Comp Sci & TechnolCluster & Grid Comp Lab Wuhan 430074 Peoples R China Natl Univ Singapore Dept Comp Sci Singapore 117418 Singapore Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England

With the huge demand for graph analytics in many real-world applications, massive iterative graph processing jobs are concurrently performed on the same graphs and suffer from significant high data access cost. To lower the data access cost toward high performance, several out-of-core concurrent graph processing solutions are recently designed to handle concurrent jobs by enabling these jobs to share the accesses of the same graph data. However, the set of active vertices in each partition are usually different for various concurrent jobs and also evolve with time, where some high-degree ones (or called hub-vertices) of these active vertices require more iterations to converge due to the power-law property of real-world graphs. In consequence, existing solutions still suffer from much unnecessary I/O traffic, because they have to entirely load each partition into the memory for concurrent jobs even if most vertices in this partition are inactive and may be shared by a few jobs. In this paper, we propose an efficient structure-aware storage system, called GraphSO, for higher throughput of the execution of concurrent graph processing jobs. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. The key design of GraphSO is a fine-grained storage management scheme. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions (i.e., each logical partition is a subset of active chunks) for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the

关键词： Throughput Loading partitioning algorithms Graphene Computer science Social networking (online) Runtime Iterative graph processing out-of-core concurrent jobs storage system

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Design of Probabilistic Shaping 4D Ultra High Order Modulation Format With 8APSK Pilot Aided Carrier Phase Recovery

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第12期40卷 3688-3697页

作者： Tian, Feng Wu, Tianze Ji, Yiqing Wang, Chuxuan Zhang, Qi Gao, Ran Li, Zhipei Tian, Qinghua Xin, Xiangjun Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

A new four-dimensional (4D) probabilistically shaped (PS) ultra-high order modulation format is proposed with pilot aided carrier phase recovery (PA-CPR) algorithm to improve the general mutual information (GMI) performance. While the structure of the 4D-PS ultra-high order modulation format uses constellation set partitioning (SP) based on amplitude translation, and the PA-CPR applies 8-level amplitude phase shift keying (8APSK) pilot. To demonstrate the feasibility of the scheme, the PS-1024QAM system is designed on the simulation platform. The results show that the proposed 8APSK-PA-CPR algorithm outperforms the QPSK-PA-CPR algorithm. The gain of the optimal 8APSK-PA-CPR is similar to 0.4 dB compared with the quadrature phase-shift keying (QPSK) PA-CPR algorithm. The steep drop of blind phase search (BPS) with different PS factors is overcome by 8APSK-PA-CPR when the signal-to-noise (SNR) ratio is lower than 24 dB. The maximum improvement of GMI is 1.3 bit/symbol when the SNR is higher than 26 dB and the complexity of the 8APSK-PA-CPR algorithm is reduced by 99.9% compared with the BPS algorithm. The gain obtained from the proposed 4D modulation scheme is similar to 2.8 dB under the condition of GMI = 17.7971 bit/symbol. It demonstrated that this scheme has a strong tolerance to noise, low computational complexity, and high coding gain. It is a feasible and flexible scheme for PS high-order modulation format transmission systems.

关键词： partitioning algorithms Modulation Probabilistic logic Phase shift keying Signal to noise ratio Approximation algorithms Gain Carrier phase recovery coherent optical communications four-dimensional pilot aided probabilistic shaping set partitioning

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Two-Stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS Algorithm

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IEEE ACCESS 2022年 10卷 99646-99660页

作者： Sun, Qi Zhang, Haifei Dang, Jianwu Lanzhou Jiaotong Univ Key Lab Railway Ind BIM Engn & Intelligent Elect Lanzhou 730070 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Peoples R China

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the region scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam Search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

关键词： Logistics Road traffic Optimization Heuristic algorithms Search problems Vehicle routing partitioning algorithms Urban areas Vehicle routing optimization complex road network two-stage algorithm heuristic simulated annealing hybrid genetic beam search

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Enhanced Multiview Fuzzy Clustering Using Double Visible-Hidden View Cooperation and Network LASSO Constraint

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第11期30卷 4965-4979页

作者： Deng, Zhaohong Liang, Ling Yang, Hongtan Zhang, Wei Lou, Qiongdan Choi, Kup-Sze Zhang, Te Zhou, Jin Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Key Lab Computat Neurosci & Brain Inspired Intell Shanghai 200433 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China Univ Nottingham Sch Comp Sci Lab UncertaiNty Data & Decis Making Nottingham NG7 2RD England Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China

Multiview clustering is an important topic in multiview learning, where the cooperation of different views is used to improve clustering performance. Although multiview clustering has made considerable progress, most existing methods only utilize the information of the original visible views, or only consider some hidden space information shared by different views. Two of the challenges are: 1) insufficient exploitation of cooperative learning between visible and hidden information despite some preliminary attempts, and 2) inadequate consideration of topological information for improving multiview clustering. To meet the challenges, we propose the cooperation enhanced multiview fuzzy clustering method (CE-MVFC) in this article. First, we characterize multiview data with two hidden views, which are obtained by adaptive multiview non-negative matrix factorization (NMF) and fuzzy partition information of each sample in different clusters. Then, we integrated the hidden views and the original visible views to realize visible-hidden cooperation learning. Furthermore, we establish a similarity matrix for each visible view and the hidden view obtained through NMF to describe the data topology in these views. Based on the spatial topological relationship of the samples and the representation of hidden view obtained by fuzzy partition, the network least absolute shrinkage and selection operator is constructed to constrain multiview learning. Finally, we develop the multiview clustering method by exploiting the visible-hidden information cooperation and the spatial topological information constraints. Experiments on benchmark multiview datasets are conducted to demonstrate the highly competitive performance of the proposed CE-MVFC against the state-of-the-art methods.

关键词： Clustering methods Feature extraction Clustering algorithms Optimization Collaboration partitioning algorithms Data models Double hidden views multiview clustering network LASSO constrained learning visible-hidden view cooperation

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An Improved Multi-Objective Artificial Physics Optimization Algorithm Based on Multi-Strategy Fusion

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IEEE ACCESS 2022年 10卷 108736-108748页

作者： Sun, Bao Zhang, Lijing Li, Zhanlong Fan, Kai Jin, Qinqin Guo, Jin Taiyuan Univ Sci & Technol Sch Appl Sci Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Sci & Technol Sch Mech Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Sci & Technol Sch Mat Sci & Engn Taiyuan 030024 Shanxi Peoples R China

The elements of the population of the artificial physics optimization (APO) algorithm are assigned mass, velocity, and displacement attributes. It is a new type of heuristic algorithm, but it still has defects such as low efficiency of non-dominated solution selection and unbalanced search capacity of global and local. This paper introduces the mechanism of improved fast non-dominant sorting and partition-guided individual evolution into the APO algorithm to overcome this imperfection. An improved multi-objective artificial physics optimization algorithm based on fast non-dominated sorting (IFNS-MOAPO) is proposed, which is the result of integrating numerous strategies into the APO algorithm. Firstly, an improved fast non-dominated sorting strategy is introduced. This strategy can increase the efficiency of selecting non-dominated solutions and decrease the running time of the algorithm. Secondly, the mechanism of individual evolution guided by partition is proposed. For individuals in infeasible and feasible domains, different mass functions and virtual force calculation rules are adopted to update the algorithm iteratively to boost the convergence performance. To verify the comprehensive performance of the IFNS-MOAPO algorithm, eleven benchmark test problems are selected for simulation experiments and compared with five algorithms in terms of runtime duration, Pareto front plots, and metric values. The results show that the IFNS-MOAPO algorithm has good distribution and can converge to the true Pareto front quickly. It is a useful tool for solving constrained multi-objective optimization problems (CMOPs).

关键词： Optimization Runtime partitioning algorithms Statistics Social factors Sorting Physics Heuristic algorithms Artificial physics optimization mass function multi-objective optimization partition evolution

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Progressive Entity Matching via Cost Benefit Analysis

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IEEE ACCESS 2022年 10卷 3979-3989页

作者： Sun, Chenchen Hou, Zhijiang Shen, Derong Nie, Tiezheng Tianjin Univ Technol Minist Educ Key Lab Comp Vis & Syst Tianjin 300384 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Tianjin Univ Technol Lib Tianjin 300384 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

Entity matching (EM) is a fundamental problem in data preprocessing, and is a long running topic in big data analytics and mining communities. In big data era, (nearly) real-time data applications become popular, and call for progressive EM, which produces as many match pairs as possible in very limited time. Previous progressive EM focus on memory based solutions, but disk based solutions are necessary when dirty datasets cannot be fully loaded into memory. To this end, we propose a cost benefit analysis based progressive EM approach, which partitions data according to coarse clustering results and then iteratively schedules data partitions in a greedy way for high progressive resolution. At first, based on estimated record pair similarities, records are fast coarsely clustered;then, record clusters with near average similarities are greedily distributed to the same partitions, and data partitions are cached. After that, cost model is defined with time and space constrains, and benefit model is defined with expected resolution results. On the basis of the cost benefit model, a greedy approximate method is proposed to effectively schedule data for high progressiveness of EM. Finally, we implement extensive experiments over several datasets to evaluate our approach, and show its advantages over existing works.

关键词： Schedules partitioning algorithms Estimation Sun Sorting Scheduling Licenses Entity matching progressive cost benefit model data partitioning data integration

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Adaptive Estimation Distribution Distributed Differential Evolution for Multimodal Optimization Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 6059-6070页

作者： Wang, Zi-Jia Zhou, Yu-Ren Zhang, Jun Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou 510006 Peoples R China Victoria Univ Melbourne Vic 8001 Australia

Multimodal optimization problems (MMOPs) require algorithms to locate multiple optima simultaneously. When using evolutionary algorithms (EAs) to deal with MMOPs, an intuitive idea is to divide the population into several small "niches," where different niches focus on locating different optima. These population partition strategies are called "niching" techniques, which have been frequently used for MMOPs. The algorithms for simultaneously locating multiple optima of MMOPs are called multimodal algorithms. However, many multimodal algorithms still face the difficulty of population partition since most of the niching techniques involve the sensitive niching parameters. Considering this issue, in this article, we propose a parameter-free niching method based on adaptive estimation distribution (AED) and develop a distributed differential evolution (DDE) algorithm, which is called AED-DDE, for solving MMOPs. In AED-DDE, each individual finds its own appropriate niche size to form a niche and acts as an independent unit to find a global optimum. Therefore, we can avoid the difficulty of population partition and the sensitivity of niching parameters. Different niches are co-evolved by using the master-slave multiniche distributed model. The multiniche co-evolution mechanism can improve the population diversity for fully exploring the search space and finding more global optima. Moreover, the AED-DDE algorithm is further enhanced by a probabilistic local search (PLS) to refine the solution accuracy. Compared with other multimodal algorithms, even the winner of CEC2015 multimodal competition, the comparison results fully demonstrate the superiority of AED-DDE.

关键词： Statistics Sociology partitioning algorithms Optimization Space exploration Clustering algorithms Sensitivity Adaptive estimation distribution (AED) distributed differential evolution (DDE) multimodal optimization problems (MMOPs) niching techniques

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Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3921-3934页

作者： Tuncer, Barkin Orguner, Umut Ozkan, Emre Middle East Tech Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive filtering framework. We utilize the variational Bayes inference method to solve the association problem and to approximate the intractable true posterior. The performance of the proposed solution is demonstrated in simulations and real-data experiments. The results show that our method outperforms the state-of-the-art methods in terms of accuracy with lower computational complexity.

关键词： Kinematics Covariance matrices Shape Target tracking partitioning algorithms Signal processing algorithms Computational modeling Extended target tracking random matrix variational Bayes

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Yet Another Computation-Oriented Necessary and Sufficient Condition for Stabilizability of Switched Linear Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第7期67卷 3627-3632页

作者： Fiacchini, Mirko Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France

This article presents a computational method to test the stabilizability of discrete-time switched linear systems. The existence of a conic cover of the space on whose elements a convex condition holds is proved to be necessary and sufficient for stabilizability. An algorithm for computing a conic partition that satisfies the new necessary and sufficient condition is given. The algorithm, which allows us to determine bounds on the exponential convergence rate, is proved to overcome the conservatism of conditions equivalent to periodic stabilizability and is applied to a 4-D system.

关键词： Switches Switched systems Linear systems Convergence Stability analysis Heuristic algorithms partitioning algorithms Convex analysis stabilizability switched linear systems

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Fast Power Grid Partition for Voltage Control With Balanced-Depth-Based Community Detection Algorithm

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第2期37卷 1612-1622页

作者： Yang, Yang Sun, Yichao Wang, Qi Liu, Fusuo Zhu, Ling Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China NARI Grp Corp Dept Elect Network Anal Nanjing 210097 Peoples R China

Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-scale networks. Since complex operation characteristics and topology have emerged in recent power systems, the power grid partition requires higher efficiency and quality. Therefore, this paper proposes a fast network partition method with a balanced-depth-based community detection algorithm. Its aim is to significantly improve the efficiency of partition while maintaining high quality of partition, with which the inter-zone coupling is minimized while the intra-zone coupling is maximized. In the meantime, a surrogate-optimization-based selection algorithm is proposed to select the zonal pilot bus, based on which the secondary voltage control method is used to evaluate the quality of partition. Results from four case studies conducted in various power networks with different sizes, as compared to other partition methods, validate the high efficiency and high quality of the proposed power grid partition approach.

关键词： Detection algorithms Power grids partitioning algorithms Voltage measurement Power measurement Sensitivity Sun Community detection algorithm electrical distance hierarchical clustering algorithm voltage control power network partition modularity pilot bus

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