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Adaptive Estimation Distribution Distributed Differential Evolution for Multimodal Optimization Problems

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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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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Efficient Density Estimation for High-Dimensional Data

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IEEE ACCESS 2022年 10卷 16592-16608页

作者： Majdara, Aref Nooshabadi, Saeid Michigan Technol Univ Dept Elect & Comp Engn Houghton MI 49931 USA

Multivariate density estimation methods typically work well in low dimensions and their extension to data analytics in high dimensions domain has proven challenging. For density estimation in high-dimensional big data domains, the non-parametric Bayesian sequential partitioning (BSP) algorithm provides an efficient way of partitioning the sample space, based on Bayesian inference. In this paper, we present a detailed analysis of BSP and provide a computationally efficient copula-transformed data structure and algorithm for use in density estimation for data analytics in high dimensions. Using the copula-transformed data structure, we implement the density estimation for marginals in both BSP and kernel density estimation (KDE) methods. The data structures and algorithm are suitably designed for most efficient rendering into parallel processing paradigms of open multi-processing (OPENMP(R)) and message passing interface (MPI).

关键词： Estimation Bayes methods partitioning algorithms Histograms Kernel Data structures Licenses Multivariate density estimation non-parametric high-dimensional Bayesian sequential partitioning copula transform

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Fast Generation of Superpixels With Lattice Topology

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 4828-4841页

作者： Pan, Xiao Zhou, Yuanfeng Zhang, Yunfeng Zhang, Caiming Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250100 Peoples R China Shandong Univ Finance & Econ Shandong Key Lab Digital Media Technol Jinan 250100 Peoples R China Shandong Univ Sch Software Jinan 250012 Peoples R China Shandong Coinnovat Ctr Future Intelligent Comp Yantai 264005 Peoples R China

Serving as an essential step for many applications of image processing, superpixel generation has attracted a lot of attentions. Most existing superpixel generation algorithms focus on the boundary adherence and compactness of the superpixels, but ignore the topological consistency between the superpixels, which severely limites their applications in the subsequent tasks, especially in the CNN based image processing tasks. In this paper, we present a fast lattice superpixel generation algorithm, which can generate superpixels with lattice topology like the original pixels. We also propose a local similarity loss function to improve the segmentation accuracy of the generated lattice superpixels. The whole algorithm is parallelly implemented on GPU. We perform extensive experiments on three datasets (i.e., BSDS500, NYUv2 and VOC) to verify the efficacy of our algorithm. The experimental results show that our method achieves competitive results compared to the state-of-the-art methods.

关键词： Lattices Topology Clustering algorithms Image segmentation partitioning algorithms Task analysis Deep learning Superpixels lattice topology similarity loss deep learning

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PPD: A Scalable and Efficient Parallel Primal-Dual Coordinate Descent Algorithm

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第4期34卷 1958-1966页

作者： Wu, Hejun Huang, Xinchuan Luo, Qiong Yang, Zhongheng Sun Yat Sen Univ Dept Comp Sci Guangdong Key Lab Big Data Anal & Proc Guangzhou 510275 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Comp & Engn Hong Kong Peoples R China

Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data elements causes result inaccuracy and slow convergence, due to the concurrent updates of multiple coordinates. Some parallelization methods adopt separable approximate functions that depend on the degree of parallelism. Such dependencies result in both poor scalability and slow-convergence. To address these challenges, in this paper we present a new parallel primal-dual algorithm for DCD, called PPD. In PPD, the block data distribution is utilized to obtain a new approximate function that is independent from the parallelism. Moreover, PPD is designed with a novel primal-dual acceleration scheme to approach the optimal solution closely and quickly. We demonstrate the advantages of PPD in terms of scalability and efficiency through experiments.

关键词： Optimization Approximation algorithms Convergence Parallel processing partitioning algorithms Scalability Message systems Parallel computing dual coordinate descent convex optimization classification machine learning

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Isolation Forest Based on Minimal Spanning Tree

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IEEE ACCESS 2022年 10卷 74175-74186页

作者： Galka, Lukasz Karczmarek, Pawel Tokovarov, Mikhail Lublin Univ Technol Dept Comp Sci PL-20618 Lublin Poland

Detecting anomalies in data sets has been one of the most studied issues in modern data analysis. Therefore, there is a plethora of applications in a very wide range of fields of science and technology. One of the most frequently used anomaly detection methods is Isolation Forest. In this study, we propose a novel efficient approach based on this technique. In order to improve the classification accuracy of the base method, we make two-fold modifications. First, we propose a change of the technique of building isolation trees to merge nodes by minimal spanning tree algorithm. The second change is based on a modification of the function assessing the anomaly of the analyzed element (data record) to sum of factors correlated with tree height and nearest point distance. In the series of comprehensive computational experiments, the proposed method has proven to produce better results than other compared state-of-the-art methods available in popular data mining programming libraries. It is worth stressing that the final version of the new method in comparison to original Isolation Forest is 2.9% better in terms of AUC measure.

关键词： Vegetation Forestry Anomaly detection partitioning algorithms Merging Hidden Markov models Entropy Anomaly detection isolation forest minimal spanning tree outliers detection

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A Distance Metric for Uneven Clusters of Unsupervised K-Means Clustering Algorithm

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IEEE ACCESS 2022年 10卷 86286-86297页

作者： Raeisi, Mostafa Sesay, Abu B. Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada

In this paper, we propose a new distance metric for the K-means clustering algorithm. Applying this metric in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster with a centroid away from the origin, rather than a cluster closer to the origin. The proposed metric is based on the Canberra distances and it is useful for cases that require unequal size clusters. This metric can be used in connected autonomous vehicle wireless networks to classify mobile users such as pedestrians, cyclists, and vehicles. We use a combination of mathematical and exhaustive search to establish its validity as a true distance metric. We compare the K-Means algorithm using the proposed distance metric with five other distance metrics for comparison. These metrics include the Euclidean, Manhattan, Canberra, Chi-squared, and Clark distances. Simulation results depict the effectiveness of our proposed metric compared with the other distance metrics in both one-dimensional and two-dimensional randomly generated datasets. In this paper, we use three internal evaluation measures namely the Compactness, Sum of Squared Errors (SSE), and Silhouette measures. These measures are used to study the proper number of clusters for each of the K-Means algorithms and also select the best run among multiple centroid initializations. The elbow method and the local maximum approach are used alongside the evaluation measures to select the optimal number of clusters.

关键词： Measurement Clustering algorithms Classification algorithms partitioning algorithms Euclidean distance Road traffic Training data Unsupervised learning Canberra distance chi-squared distance clustering algorithm distance metrics Euclidean distance K-means algorithm unsupervised learning

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A Practical Energy/Power Reduction Approach for Parallel Decimal Multiplier

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IEEE ACCESS 2022年 10卷 11372-11381页

作者： Gorgin, Saeid Nejad, Helia Zareie Lee, Jeong-A Chosun Univ Dept Comp Engn Gwangju 61452 South Korea Iranian Res Org Sci & Technol Dept Elect Engn & Informat Technol Tehran *** Iran Islamic Azad Univ Qazvin Branch Comp & Informat Technol Engn Qazvin 341851416 Iran

Decimal computation is highly demanded in many human-centric applications such as banking, accounting, tax calculation, and currency conversion. Hence the design and implementation of radix-10 arithmetic units attract the attention of many researchers. Among the basic decimal arithmetic operations, multiplication is not only a frequent operation but also has high complexity and considerable power consumption. Therefore, this paper concentrates on this issue and studies a general design methodology that reduces power/energy consumption via localizing switching activity without compromising target performance. This method decomposes a digit multiplier into smaller ones, like Karatsuba's algorithm, while the multiplicand and the multiplier can be partitioned into different sizes. We take advantage of various size partitions in two types of symmetric and asymmetric, which final designs provide specific characteristics. All designs were implemented using VHDL and synthesized in the Design-Compiler toolbox with TSMC 130 nm Technology file. The results are significant;in respect to the original design, by a random test vector, 25% power reduction is achieved with no effect on latency and a negligible area penalty. Moreover, the experimental results indicate a potential for considerable reduction of power dissipation based on statistical properties of possible input data.

关键词： Arithmetic Switches Power demand partitioning algorithms Heuristic algorithms Hardware Licenses Decimal computation digital arithmetic low power design parallel multiplier

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TaiChi: A Hybrid Compression Format for Binary Sparse Matrix-Vector Multiplication on GPU

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第12期33卷 3732-3745页

作者： Gao, Jianhua Ji, Weixing Tan, Zhaonian Wang, Yizhuo Shi, Feng Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

Binary Sparse Matrix-Vector Multiplication (SpMV) is a heavy computational kernel in weblink analysis, integer factorization, compressed sensing, spectral graph theory, and other domains. Testing several popular GPU-based SpMV implementations on 400 sparse matrices, we observed that data transfer to GPU memory accounts for a large part of the total computation time. The transfer of constant value "1"s can be easily eliminated for binary sparse matrices. However, compressing index arrays has always been a great challenge. This article proposes a new compression format TaiChi to further reduce index data copies and improve the performance of SpMV, especially for diagonally dominant binary sparse matrices. Input matrices are first partitioned into relatively dense and ultra-sparse areas. Then the dense areas are encoded inversely by marking "0"s, while the ultra-sparse area is encoded by marking "1"s. We also designed a new SpMV algorithm only using addition and subtraction for binary matrices based on our partition and encoding format. Evaluation results on real-world binary sparse matrices show that our hybrid encoding for binary matrix significantly reduces the data transfer and speeds up the kernel execution. It achieves the highest transfer and kernel execution speedups of 5.63x and 3.84x on GTX 1080 Ti, 3.39x and 3.91x on Tesla V100.

关键词： Sparse matrices Kernel Graphics processing units Indexes Arrays Encoding partitioning algorithms Binary sparse matrix SpMV GPU parallel computing mathematical morphology

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