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2nd IEEE International conference on Big Data Security on Cloud (BigDataSecurity)

作者： Wang, Xu Zhu, Yongxin Chen, Yufeng Shanghai Jiao Tong Univ Sch Microelect 800 DongChuan Rd Shanghai Peoples R China Co State Grid Elect Power Res Inst Shandong Power Supply Jinan Shandong Peoples R China

ISBN: (纸本)9781509024032

With the prevalence of graph data in real-world applications and their ever-increasing size, many graph computing systems have been developed in recent years to scale the processing and analyzing of massive graphs. However, few study focuses on modeling the performance of graph algorithms and systems to identify the bottleneck of existing machines under the workload of large-scale graphs. In this paper, we propose an analytical performance model considering computation capacity, communication bandwidth and communication latency to quantify the performance of graph algorithms. Based on this model, we find the majority of graph processing runtime is spent on random access of neighbouring vertices, causing a high cache miss rate, which makes the performance bounded by communication latency. We further present performance optimization methods from perspectives of both hardware and software to alleviate the bottlenecks. Experiment results from both synthesis graphs and real graphs show that these optimization methods can produce up to 4X performance improvement in a variety of graph algorithms.

关键词： Graph performance analysis performance model Cache optimization

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An Improved Blind optimization Algorithm for Hardware/software Partitioning and Scheduling 9th

An Improved Blind Optimization Algorithm for Hardware/Softwa...

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9th International conference on Swarm Intelligence (ICSI)

作者： Zhao, Xin Zhang, Tao An, Xinqi Fan, Long Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Texas Instruments DSP Joint Lab Tianjin 300072 Peoples R China

ISBN: (纸本)9783319938189;9783319938172

Hardware/software partitioning is an important part in the development of complex embedded system. Blind optimization algorithms are suitable to solve the problem when it is combined with task scheduling. To get hardware/software partitioning algorithms with higher performance, this paper improves Shuffled Frog Leaping Algorithm-Earliest Time First (SFLA-ETF) which is a blind optimization algorithm. Under the supervision of the aggregation factor, the improved algorithm named Supervised SFLA-ETF (SSFLA-ETF) used two steps to better balance exploration and exploitation. Experimental results show that compared with SFLA-ETF and other swarm intelligence algorithms, SSFLA-ETF has stronger optimization ability.

关键词： Hardware/software partitioning Blind optimization algorithms SSFLA-ETF high performance

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Soft computing-based semi-automated test case selection using gradient-based techniques

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SOFT COMPUTING 2020年第17期24卷 12981-12987页

作者： Nithya, T. M. Chitra, S. K Ramakrishnan Coll Engn Dept Comp Sci & Engn Trichy Tamil Nadu India Er Perumal Manimekalai Coll Engn Hosur Tamil Nadu India

software testing has been one very time-consuming and expensive phases in the process of software development. It needs plenty of effort in the development of tools for the process to reduce both cost and time in the development of software. The test cases were the input parameters along with expected results and conditions of execution that are used for testing. The test case selection (TCS) is approaches that aim at the selection of subsets for the test cases in a particular domain based on the criterion of interest. The primary aim is the elimination of unwanted and redundant test data aside from maximizing fault detection. optimization techniques are applied for TCS to efficient testing. A local method of search is very popular among algorithms for the performance of optimization which is known as the gradient descent. This makes use of structural information from the nonlinear model. Simulated annealing (SA), on the other hand, is one which is global heuristic that minimizes the cost function. This work has proposed a high level of gradient descent and simulated annealing for the selection of software test case. Experiments showed that the modified SA has a lower number of print tokens by 3.82% for the 10,000 cost, by about 2.5% for the 30,000 cost, by about 1.17% for the 50,000 cost and finally by about 1.14% for the 70,000 cost.

关键词： software testing Test cases (TC) Test case selection (TCS) Gradient descent (GD) Simulated annealing (SA) high gradient descending simulated annealing

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A modified PSO algorithm with line search

A modified PSO algorithm with line search

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International conference on Computational Intelligence and software Engineering

作者： Zeng, Guanghui Jiang, Yuewen Department of Electronic and Information Engineering Guangzhou City Construction College Conghua China School of Physics and Optical Information Sciences Jiaying University Meizhou China

ISBN: (纸本)9781424453924

Recently Particle Swarm optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improved by using hybrid techniques. There are various hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristics algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and basic PSO (B-PSO). The performance of the proposed hybrid algorithm, examined through four typically nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms basic PSO. ©2010 IEEE.

关键词： Particle swarm optimization (PSO)

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A Coupled Spring Forced Bat Searching Algorithm: Design, Analysis and Evaluation

A Coupled Spring Forced Bat Searching Algorithm: Design, Ana...

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American Control conference (ACC)

作者： Zhang, Haopeng Hui, Qing Univ Louisville Dept Mech Engn Louisville KY 40292 USA Univ Nebraska Lincoln Dept Elect & Comp Engn Lincoln NE 68588 USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682661

Swarm intelligence based optimization algorithms mimic the cooperation and interaction behaviors from social or nature phenomena as an optimization mechanism to solve complex, non-convex and/or ill-conditioned general nonlinear problems with high efficiency. Recently, a novel and heuristic algorithm, Bat searching algorithm (BA) has been proposed. Moreover, numerical evaluation has already demonstrated the better performance of BA compared with other algorithms variations. In this paper, we propose a coupled spring forced BA (SFBA) algorithm by considering that each particle is a spring and is coupled with the optimal solution found so far as the second abstract spring. The synergistic integration of the coupled springs, the bat's behavior, and swarm intelligence governs and navigates the new algorithm in the searching process. Moreover, the convergence of the SFBA is studied via Jury's Test. Numerical evaluation is provided for the proposed SFBA algorithm by conducting comparison with other variations of BA in the literature, which indicates that the performance of SFBA surpasses all the listed variations of BA significantly. In summary, the proposed SFBA algorithm offers a new efficient approach to address complex, large-scale, non-convex nonlinear optimization problems which are normally hard to solve using the conventional methods.

关键词： Springs optimization Heuristic algorithms Particle swarm optimization Search problems Convergence Trajectory

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Adaptive Aggregative Multitask Competitive Particle Swarm optimization with Bi-Directional Asymmetric Flip Strategy for high-Dimensional Feature Selection

Adaptive Aggregative Multitask Competitive Particle Swarm Op...

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Genetic and Evolutionary Computation conference (GECCO)

作者： Zhang, Yong Du, Ke-Jing Jiang, Yi Wang, Li-Min Wang, Hua Zhan, Zhi-Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic 8001 Australia Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou 510320 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

ISBN: (纸本)9798400704949

Evolutionary multitask optimization (EMTO) has been increasingly employed in addressing high-dimensional feature selection challenges, but current EMTO algorithms still have three deficiencies: First, in task generation, they just consider the linear correlation among features but ignore the nonlinear correlation. Second, they commonly encounter negative knowledge transfer. Third, they are hard to strike an optimal balance between global search capability and computational efficiency. This paper proposes an adaptive aggregative multitask competitive swarm optimization (AAMCSO) for high-dimensional feature selection, which contains three novel and effective strategies to address the above deficiencies. Firstly, AAMCSO proposes a linear-and-nonlinear-correlation-based task generation strategy to generate multiple tasks while considering both linear and nonlinear correlation between features and labels. Secondly, AAMCSO proposes an adaptive aggregative knowledge transfer strategy to adaptively transfer positive knowledge among related tasks. Thirdly, AAMCSO proposes a bi-directional asymmetric flip strategy to guide the population to search for a smaller feature subset with better classification performance. We have conducted extensive comparative experiments on AAMCSO and multiple state-of-the-art feature selection algorithms in high-dimensional feature selection problems with up to 10000 dimensions. The results show that AAMCSO achieves significantly superior performance to the state-of-the-art comparison algorithms in terms of both classification accuracy and feature number.

关键词： Evolutionary multitask optimization high-dimensional feature selection Particle swarm optimization Competitive swarm optimization Evolutionary computation

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A Pair-wise Bare Bones Particle Swarm optimization Algorithm 16

A Pair-wise Bare Bones Particle Swarm Optimization Algorithm

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16th IEEE/ACIS International conference on Computer and Information Science (ICIS)

作者： Guo, Jia Sato, Yuji Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan

ISBN: (纸本)9781509055074

Bare bones particle swarm optimization (BBPSO) algorithm, a swarm intelligence algorithm, is famous for its easy applying and parameter-free. That is why its principles and applications have been studied by a lot of scholars in recent years. However, quickly losing the diversity of the swarm still causes the premature convergence in the iteration process. Hence, a pair-wise bare bones particle swarm optimization (PBBPSO) algorithm is proposed in this paper to balance the exploration and exploitation. Moreover, a separate iteration strategy is used in pair-wise operator to enhance the diversity of the swarm. Also, to verify the performance of the proposed algorithm, a set of well-known nonlinear benchmark functions are used in the experiment. Furthermore, severe variants of BBPSO and some other evolutionarily algorithms are also evaluated on the same functions as the control group. Finally, the experiment result and statistical analysis confirm the performance of PBBPSO with nonlinear functions.

关键词： Bare bones particle swarm optimization pair-wise diversity increasing

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A Logistic Distribution Routes Solving Strategy Based on the Physarum Network and Ant Colony optimization Algorithm 17

A Logistic Distribution Routes Solving Strategy Based on the...

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2015 IEEE 17th International conference on high performance Computing and Communications (HPCC)

作者： Chen, Qianguo Qian, Tao Liu, Kui Chongqing Technol & Business Univ Ctr Teaching & Learning Dev Chongqing Peoples R China Bank Chongqing Software Dev Ctr Chongqing Peoples R China Southwest Univ Expt Teaching Ctr Int Studies Chongqing Peoples R China

ISBN: (纸本)9781479989379

The Physarum Network with single inlet and multi outlet model (SMPN) exhibits a unique feature that the critical pipelines are reserved with the evolution of network. In addition, ant colony optimization algorithm is a classic optimization algorithm of simulated evolutionary algorithms, which has been used to solve optimal scheduling problems. In this paper, drawing on this feature, an optimized Ant Colony optimization (ACO) algorithm denoted as SMPNACO algorithm is proposed based on the Physarum Network and Ant Colony optimization Algorithm (ACO) to solve the Vehicle Routing Problem (VRP). Throughout the algorithm, the amount of pheromone flowed in network are related to the customers' requirement. When the pheromone matrix is updated, the SMPNACO algorithm updates both the pheromone released by ants and the flowing pheromone in the Physarum Network. By adding extra pheromones in the Physarum Network improves the convergence performance of Ant Colony optimization algorithm. The simulative experiments show that the SMPNACO algorithm is less affected by the initial total pheromone, this algorithm is feasible in solving the small scale VRP, and can effectively solve the VRP.

关键词： Physarum Network model Ant Colony optimization algorithm Logistics distribution routes optimization VRP

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Deep Pipelining: Efficient Pipelining of Network Function Chains with Coroutines 5

Deep Pipelining: Efficient Pipelining of Network Function Ch...

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5th IEEE conference on Network Softwarization (IEEE NetSoft) / 2nd International Workshop on Advances in Slicing for Softwarized Infrastructures (S4SI)

作者： Asai, Hirochika Preferred Networks Inc Tokyo Japan

ISBN: (纸本)9781538693766

Network Functions Virtualization emphasizes the importance of performance in software-based network functions and in-network computing on general-purpose computers. Efficient resource utilization is challenging to run multiple network functions at high performance. This paper proposes a generalized performance optimization technique for chains of network functions on commodity CPUs, named deep pipelining. The key idea of deep pipelining is the combination of software pipelining and coroutines to reduce the overhead of high latency instructions such as memory access of network algorithms. Coroutines are used to schedule the execution of multiple pipelines of chained network functions. Microbenchmark and synthetic benchmark results shed light on both the effectiveness and the small overhead of deep pipelining. By taking the advantage of deep pipelining, we prove the effectiveness in chaining network functions with software-based layer-3 switch implementation consisting of Ethernet switching and IP routing on our operating system. The evaluation demonstrates that deep pipelining improves packet switching and routing performance by 0.11-78% compared to the existing batching-with-prefetch approach.

关键词： Pipeline processing software algorithms Switches Hardware Routing Prefetching Pipeline processing software algorithms prefetching Switches Network routing Computer hardware symbiotes Network

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Determination of the design parameters of passive harmonic filters using nonlinear optimization.

Determination of the design parameters of passive harmonic f...

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IEEE Industrial and Commercial Power Systems Technical conference

作者： Upadhye, SD Atre, YR Tata Infotech Ltd Pune 400096 Maharashtra India

ISBN: (纸本)0780345096

Power system harmonics are a menace with disastrous consequences. A number of ways were tried out to tackle the harmonic problem posed by nonlinear electric devices, but by far, the most popular and quite effective one is the use of passive filters. This provides a low impedance path to a harmonic or a group of harmonics. Usually the AC harmonics are odd harmonics. So normally the filter combination selected is such that for the predominant harmonics, one or more single tuned filters are used, and for the rest of the harmonics, a high pass filter is used. An alternate combination is that a damped filter is used for the two predominant harmonics with the filter being tuned at a frequency in between the two harmonics and another damped filter for the higher harmonics. If all the harmonics are not of very high magnitude then usually one high pass filter is sufficient for all the harmonics. Instead of the conventional design of these filters, which is based on experience of the designer, a better way is to use the optimization algorithms to obtain maximum performance from these filters. © 1998 IEEE.

关键词： high pass filters

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