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Balancing Constraints and Objectives by Considering Problem Types in Constrained Multiobjective Optimization

IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 88-101页

作者： Xiang, Yi Yang, Xiaowei Huang, Han Wang, Jiahai South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Software Engn Minist Educ Guangzhou 510006 Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

Constrained multiobjective optimization problems widely exist in real-world applications. To handle them, the balance between constraints and objectives is crucial, but remains challenging due to non-negligible impacts of problem types. In our context, the problem types refer particularly to those determined by the relationship between the constrained Pareto-optimal front (PF) and the unconstrained PF. Unfortunately, there has been little awareness on how to achieve this balance when faced with different types of problems. In this article, we propose a new constraint handling technique (CHT) by taking into account potential problem types. Specifically, inspired by the prior work, problems are classified into three primary types: 1) I;2) II;and 3) III, with the constrained PF being made up of the entire, part and none of the unconstrained counterpart, respectively. Clearly, any problem must be one of the three types. For each possible type, there exists a tailored mechanism being used to handle the relationships between constraints and objectives (i.e., constraint priority, objective priority, or the switch between them). It is worth mentioning that exact problem types are not required because we just consider their possibilities in the new CHT. Conceptually, we show that the new CHT can make a tradeoff among different types of problems. This argument is confirmed by experimental studies performed on 38 benchmark problems, whose types are known, and a real-world problem (with unknown types) in search-based software engineering. Results demonstrate that within both decomposition-based and nondecomposition-based frameworks, the new CHT can indeed achieve a good tradeoff among different problem types, being better than several state-of-the-art CHTs.

关键词： Optimization Switches Statistics Sociology software engineering Taxonomy software algorithms Constrained multiobjective optimization constraint handling techniques (CHTs) problem types tradeoff model (ToM)

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Onboard Sensors-Based Self-Localization for Autonomous Vehicle With Hierarchical Map

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第7期53卷 4218-4231页

作者： Xia, Chao Shen, Yanqing Yang, Yuedong Deng, Xiaodong Chen, Shitao Xin, Jingmin Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Localization is a fundamental and crucial module for autonomous vehicles. Most of the existing localization methodologies, such as signal-dependent methods (RTK-GPS and Bluetooth), simultaneous localization and mapping (SLAM), and map-based methods, have been utilized in outdoor autonomous driving vehicles and indoor robot positioning. However, they suffer from severe limitations, such as signal-blocked scenes of GPS, computing resource occupation explosion in large-scale scenarios, intolerable time delay, and registration divergence of SLAM/map-based methods. In this article, a self-localization framework, without relying on GPS or any other wireless signals, is proposed. We demonstrate that the proposed homogeneous normal distribution transform algorithm and two-way information interaction mechanism could achieve centimeter-level localization accuracy, which reaches the requirement of autonomous vehicle localization for instantaneity and robustness. In addition, benefitting from hardware and software co-design, the proposed localization approach is extremely light-weighted enough to be operated on an embedded computing system, which is different from other LiDAR localization methods relying on high-performance CPU/GPU. Experiments on a public dataset (Baidu Apollo SouthBay dataset) and real-world verified the effectiveness and advantages of our approach compared with other similar algorithms.

关键词： Location awareness Laser radar Simultaneous localization and mapping Autonomous vehicles Robustness Real-time systems software algorithms Autonomous vehicle localization homogeneous registration method normal distribution transform (NDT)-EKF tightly coupled algorithm software-hardware co-design

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Low-latency Hardware Architecture for VDF Evaluation in Class Groups

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IEEE TRANSACTIONS ON COMPUTERS 2023年第6期72卷 1706-1717页

作者： Zhu, Danyang Tian, Jing Li, Minghao Wang, Zhongfeng Nanjing Univ Sch Elect Sci & Engn Nanjing 210046 Peoples R China

The verifiable delay function (VDF), as a kind of cryptographic primitives, has recently been adopted quite often in decentralized systems. Highly correlated to the security of VDFs, the fastest implementation for VDF evaluation is generally desired to be publicly known. In this paper, for the first time, we propose a low-latency hardware implementation for the complete VDF evaluation in the class group by jointly exploiting optimizations. On one side, we reduce the required computational cycles by decreasing the hardware-unfriendly divisions and increase the parallelism of computations by reducing the data dependency. On the other side, we provide low-latency large-number divisors, multipliers, and adders, respectively, while those operators are generally very hard to be accelerated. Besides, we carefully schedule the sub-modules and devise the low-latency architecture for the complete VDF evaluation. Finally, the proposed design is coded and synthesized under the TSMC 28-nm CMOS technology. The experimental results show that our design can achieve a speedup of 3.5x compared to the optimal C++ implementation for the VDF evaluation over an advanced CPU. Moreover, compared to the state-of-the-art hardware implementation for the squaring, a key step of VDF, we achieve about 2x speedup.

关键词： Low latency communication Hardware Security Delays Computer architecture Blockchains software algorithms ASIC blockchain class groups extended GCD low-latency verifiable delay functions

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A Parallel Algorithm to Accelerate DEVS Simulations in Shared Memory Architectures

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第5期34卷 1609-1620页

作者： Trabes, Guillermo German Wainer, Gabriel A. Gil-Costa, Veronica Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Univ Nacl San Luis RA-D5700 San Luis Argentina Natl Univ San Luis RA-D5700 San Luis Argentina CCT CONICET RA-D5700 San Luis Argentina

We propose a new algorithm for the execution of Discrete Event System Specification (DEVS) simulations on parallel shared memory architectures. Our approach executes parallel discrete-event simulations by executing all tasks in the PDEVS simulation protocol in parallel. The algorithm works by distributing the computations among different cores on shared memory architectures. To show the benefits of our algorithm, we present the results of a set of experiments using a synthetic benchmark and a real-world scenario using two independent computer architectures. The results obtained show how our algorithm accelerates simulations up to eight times, improving previous approaches. In addition, we show that our approach scales when we increase the number of CPU-cores used.

关键词： Computational modeling Protocols Synchronization Cadmium Task analysis Approximation algorithms software algorithms Discrete-event parallel algorithms simulation shared memory

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Frame-Level Rate Control for Geometry-Based LiDAR Point Cloud Compression

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 3855-3867页

作者： Li, Li Li, Zhu Liu, Shan Li, Houqiang Univ Sci & Technol China CAS Key Lab Technol Geo Spatial Informat Proc & A Hefei 230027 Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA Tencent Amer 661 Bryant St Palo Alto CA 94301 USA

The state-of-the-art compression method for Light Detection And Ranging (LiDAR) point clouds is the geometry-based point cloud compression (G-PCC) standard developed by Moving Pictures Experts Group immersive media working group (MPEG-I). However, there are currently no rate control algorithms designed specifically for Geometry-based LiDAR point cloud compression (G-LPCC). In this paper, we propose the first frame-level rate control algorithm for G-LPCC. We mainly have the following contributions in our proposed rate control algorithm. First, we model the rate-distortion (R-D) relationship for both the geometry and attribute. As the geometry bitrate is mainly determined by the frame-level geometry quantizer QG, we propose a relationship between the geometry bitrate and QG. In addition, as the attribute bitrate can be influenced by both the attribute quantizer QA and QG, we build a relationship among the attribute bitrate, QG, and QA. Second, we propose a bit allocation algorithm between the geometry and attribute based on the R-D modeling. The QG and QA are modeled into a proper relationship to obtain geometry and attribute bits to achieve good R-D performance. Third, we propose using the point density of LiDAR point clouds to estimate the geometry model parameters. The point density is calculated using the average distance between each point and its nearest neighbor after excluding some noisy points. The proposed rate control algorithm is implemented in the G-PCC reference software. The experimental results show that the proposed rate control algorithm can control the bitrate accurately with satisfactory R-D performance.

关键词： Bit rate Point cloud compression Geometry Laser radar Parameter estimation Standards software algorithms Bit allocation geometry-based point cloud compression LiDAR point cloud rate control rate control model parameter estimation

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Solving the Workflow Satisfiability Problem Using General Purpose Solvers

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年第6期20卷 4474-4485页

作者： Karapetyan, Daniel Gutin, Gregory Univ Nottingham Sch Comp Sci Nottingham NG7 2RD England Univ London Royal Holloway Dept Comp Sci London WC1E 7HU England

The workflow satisfiability problem (WSP) is a well-studied problem in access control seeking allocation of authorised users to every step of the workflow, subject to workflow specification constraints. It was noticed that the number kof steps is typically small compared to the number of users in the real-world instances of WSP;therefore k is considered as the parameter in WSP parametrised complexity research. While WSP in general was shown to be W[1]-hard, WSP restricted to a special case of user-independent (UI) constraints is fixed-parameter tractable (FPT). However, restriction to the UI constraints might be impractical. To efficiently handle non-UI constraints, we introduce the notion of branching factor of a constraint. As long as the branching factors of the constraints are relatively small and the number of non-UI constraints is reasonable, WSP can be solved in FPT time. Extending the results from Karapetyan et al. (2019), we demonstrate that general-purpose solvers are capable of achieving FPT-like performance on WSP with arbitrary constraints when used with appropriate formulations. This enables one to tackle most of practical WSP instances. While important on its own, we hope that this result will also motivate researchers to look for FPT-aware formulations of other FPT problems.

关键词： Authorization Complexity theory software Business Absorption software algorithms Computer science Authorisations constraints fixed parameter tractability workflow satisfiability problem

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Comparison and Selection of Spike Encoding algorithms for SNN on FPGA

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023年第1期17卷 129-141页

作者： Wang, Kuanchuan Hao, Xinyu Wang, Jiang Deng, Bin Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

The information in Spiking Neural Networks (SNNs) is carried by discrete spikes. Therefore, the conversion between the spiking signals and real-value signals has an important impact on the encoding efficiency and performance of SNNs, which is usually completed by spike encoding algorithms. In order to select suitable spike encoding algorithms for different SNNs, this work evaluates four commonly used spike encoding algorithms. The evaluation is based on the FPGA implementation results of the algorithms, including calculation speed, resource consumption, accuracy, and anti-noiseability, so as to better adapt to the neuromorphic implementation of SNN. Two real-world applicaitons are also used to verify the evaluation results. By analyzing and comparing the evaluation results, this work summarizes the characteristics and application range of different algorithms. In general, the sliding window algorithm has relatively low accuracy and is suitable for observing signal trends. Pulsewidth modulated-Based algorithm and step-forward algorithm are suitable for accurate reconstruction of various signals except for square wave signals, while Ben's Spiker algorithm can remedy this. Finally, a scoring method that can be used for spiking coding algorithm selection is proposed, which can help to improve the encoding efficiency of neuromorphic SNNs.

关键词： Encoding Field programmable gate arrays software algorithms Hardware Classification algorithms Approximation algorithms Neuromorphics Field programmable gate array (FPGA) spike encoding algorithms spiking neural network

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A Critical Scenario Search Method for Intelligent Vehicle Testing Based on the Social Cognitive Optimization Algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 7974-7986页

作者： Zhu, Bing Sun, Yuhang Zhao, Jian Han, Jiayi Zhang, Peixing Fan, Tianxin Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China

Intelligent vehicle testing has been a hotspot in the field of intelligent vehicles. Due to the multi-dimensional parameters and the continuity of testing scenarios, a complete test of all scenarios requires a large amount of manpower and several material resources. In critical scenarios, deficiencies in an intelligent vehicle's performance and defects of an algorithm can be exposed. Therefore, increasing the search efficiency and coverage of critical scenarios is key in improving scenario-based intelligent vehicle testing technology. In this study, a critical scenario search method for intelligent vehicle testing based on the social cognitive optimization (SCO) algorithm is proposed. This method has two main parts: global search and local search. The global search, based on the modified SCO algorithm, integrates the density peak clustering (DPC) algorithm and the cooling scheduling function, and aims to find all local aggregation areas of critical scenarios in a logical scenario space. The local search applies a multi-dimensional convolution algorithm to the global search results to find critical scenarios near the local aggregation areas. Finally, a longitudinal automatic driving algorithm is tested using the proposed method under a specified logical scenario in a simulation environment. The test results show that the proposed method can improve both the search efficiency and coverage of critical scenarios.

关键词： Testing Intelligent vehicles Optimization Search methods Clustering algorithms Concrete software algorithms Accelerated test critical scenario search intelligent vehicle social cognitive optimization

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A Reinforcement Learning Based Grammatical Inference Algorithm Using Block-Based Delta Inverse Strategy

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IEEE ACCESS 2023年 11卷 12525-12535页

作者： Haneef, Farah Sindhu, Muddassar Azam Quaid i Azam Univ Dept Comp Sci Islamabad 45320 Pakistan

A resurgent interest for grammatical inference aka automaton learning has emerged in several intriguing areas of computer sciences such as machine learning, software engineering, robotics and internet of things. An automaton learning algorithm commonly uses queries to learn the regular grammar of a Deterministic Finite Automaton (DFA). These queries are posed to a Minimum Adequate Teacher (MAT) by the learner (Learning Algorithm). The membership and equivalence queries which the learning algorithm may pose, are often capable of having their answers provided by the MAT. The three main categories of learning algorithms are incremental, sequential, and complete learning algorithms. In the presence of a MAT, the time complexity of existing DFA learning algorithms is polynomial. Therefore, in some applications these algorithms may fail to learn the system. In this study, we have reduced the time complexity of DFA learning from polynomial to logarithmic form. For this, we propose an efficient complete DFA learning algorithm;the Block based DFA Learning through Inverse Query (BDLIQ) using block based delta inverse strategy, which is based on the idea of inverse queries that John Hopcroft introduced for state minimization of a DFA. The BDLIQ algorithm possess O(|sigma|***) complexity when a MAT is available. The MAT is also made capable of responding to inverse queries. We provide theoretical and empirical analysis of the proposed algorithm. Results show that our suggested approach for complete learning;BDLIQ algorithm, is more efficient than the ID algorithm in terms of time complexity.

关键词： Learning automata Machine learning algorithms Inference algorithms Time complexity Symbols software algorithms Reinforcement learning Automaton learning algorithm complete learning inverse query machine learning reinforcement learning

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A Verifiable and Efficient Secure Sharing Scheme in Multiowner Multiuser Settings

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IEEE SYSTEMS JOURNAL 2023年第4期17卷 5798-5809页

作者： Liu, Xingchen Zhang, Shaohui Huang, Haiping Wang, Wenming Malekian, Reza Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor N Nanjing 210023 Peoples R China Anqing Normal Univ Sch Comp & Informat Anqing 246133 Peoples R China Nanjing Univ State Key Lab oratory Novel Software Technol Nanjing 210023 Peoples R China Malmo Univ Dept Comp Sci & Media Technol S-20506 Malmo Sweden

Data security has remained a challenging problem in cloud storage, especially in multiowner data sharing scenarios. As one of the most effective solutions for secure data sharing, multikeyword ranked searchable encryption (MRSE) has been widely used. However, most of the existing MRSE schemes have some shortcomings in multiowner data sharing, such as index trees generated by data owners, relevance scores in plaintext form, and lack of verification function. In this article, we propose a verifiable and efficient secure sharing scheme in multiowner multiuser settings, where the index tree is generated by the trusted authority. To achieve verifiable functionality, the blockchain-based smart contract is adopted to execute the search algorithm. Based on a distributed two-trapdoor public-key cryptosystem, the data uploaded and used are in ciphertext form, and the proposed algorithms are secure in our scheme. For improving efficiency, the encrypted data are aggregated according to the category and the Category ID-based index tree is generated. Extensive experiments are conducted to demonstrate that it can reduce the time cost of index construction by 75% and the time cost of search by 53%, approximately. Moreover, multithreaded optimization is introduced in our scheme, which can reduce the time cost of index construction by 76% and the time cost of search by 67%, approximately (with 16 threads).

关键词： Cryptography Indexes Encryption Blockchains software algorithms Servers Threat modeling Blockchain multiple data owners (DOs) searchable encryption (SE) smart contract verifiable

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