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Topology selection for particle swarm optimization

INFORMATION SCIENCES 2016年 363卷 154-173页

作者： Liu, Qunfeng Wei, Wenhong Yuan, Huaqiang Zhan, Zhi-Hui Li, Yun Dongguan Univ Technol Coll Comp Sci & Technol Dongguan 523808 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

Particle swarm optimization (PSO) uses a social topology for particles to share information among neighbors during optimization. A large number of existing literatures have shown that the topology affects the performance of PSO and an optimal topology is problem dependent, but currently there is a lack of study on this issue. In this paper, we first analyze a class of deterministic regular topologies with regard to what affect the optimality of algorithmic parameters (e.g., the number of particles and the topological degree), so as to provide a guide to topology selections for PSO. Both theoretical analysis and numerical experiments are performed and reported in detail. The theoretical analysis reveals that the optimality of algorithmic parameters is dependent on the computational budget available. In particular, the optimal number of particles increases unstrictly as the computational budget increases, while for any fixed number of particles the optimal degree decreases unstrictly as computational budget increases. The only condition is that the computational budget cannot exceed a constant measuring the hardness of the benchmark function set With a total of 198 regular topologies and 9 different numbers of particles tested on 90 benchmark functions using a recently reported data profiling technique, numerical experiments verify the theoretical derivations. Based on these results, two formulas are developed to help choose optimal topology parameters for increased ease and applicability of PSO to real-world problems. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Particle swarm optimization Topology selection Data profile algorithm design

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Video image target monitoring based on RNN-LSTM

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第4期78卷 4527-4544页

作者： Liu, Feng Chen, Zhigang Wang, Jie Cent S Univ Sch Software Changsha 410075 Hunan Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan Shandong Peoples R China Hengyang Normal Univ Coll Comp Sci & Technol Hengyang Peoples R China

Traditional image object classification and detection algorithms and strategies cannot meet the problem of video image acquisition and processing. Deep learning deliberately simulates the hierarchical structure of human brain, and establishes the mapping from low-level signals to high-level semantics, so as to achieve hierarchical feature representation of data. Deep learning technology has powerful visual information processing ability, which has become the forefront technology and domestic and international research hotspots to deal with this challenge. In order to solve the problem of target space location in video surveillance system, time-consuming and other problems, in this paper, we propose the algorithm based on RNN-LSTM deep learning. At the same time, according to the principle of OpenGL perspective imaging and photogrammetry consistency, we use 3D scene simulation imaging technology, relying on the corresponding relationship between video images and simulation images we locate the target object. In the 3D virtual scene, we set up the virtual camera to simulate the imaging processing of the actual camera, and the pixel coordinates in the video image of the surveillance target are substituted into the simulation image, next, the spatial coordinates of the target are inverted by the inverse process of the virtual imaging. The experimental results show that the detection of target objects has high accuracy, which has an important reference value for outdoor target localization through video surveillance images.

关键词： Recurrent neural network Long short-term memory Deep learning network Video image Target monitoring algorithm design

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Short-term scheduling with machine calibration

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2022年第5期29卷 3031-3050页

作者： Xu, Dehua Xu, Zhijun Xu, Limin Yu, Xianyu Nanjing Univ Finance & Econ Sch Int Econ & Business Nanjing 210023 Peoples R China East China Univ Technol Fuzhou Teachers Coll Fuzhou 344000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China

In most of the past research on scheduling, it is conventional to model machine deterioration by decreasing production speed over time. However, as is observed, there is a fair number of scenarios in practice where deterioration has a little impact on machine speed but a great impact on production accuracy and a machine is unusable unless it has been calibrated recently. In this paper, we consider a single-machine short-term scheduling issue in distributed production systems where the engineer calibrates the machines in a round-robin manner. The objective is to minimize total job completion time, which is a popular criterion indicating total holding or inventory cost. We consider two cases of the problem and show that one of them can be solved optimally by providing an extended shortest processing time first rule. As for the other case, which is intractable, we propose a polynomial-time approximation scheme based on the extended rule and an exchange procedure. Computational experiments show that the proposed polynomial-time approximation scheme is very promising even if limited number of exchangeable jobs is allowed.

关键词： scheduling calibration integrated stockpile evaluation algorithm design

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INVESTIGATION OF PROPORTIONAL LINK LINKAGE CLUSTERING METHODS

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JOURNAL OF CLASSIFICATION 1985年第2-3期2卷 239-254页

作者： DAY, WHE EDELSBRUNNER, H GRAZ TECH UNIV INST INFORMATIONSVERARBEITUNG A-8010 GRAZ AUSTRIA

Proportional link linkage (PLL) clustering methods are a parametric family of monotone invariant agglomerative hierarchical clustering methods. This family includes the single, minimedian, and complete linkage clustering methods as special cases;its members are used in psychological and ecological applications. Since the literature on clustering space distortion is oriented to quantitative input data, we adapt its basic concepts to input data with only ordinal significance and analyze the space distortion properties of PLL methods. To enable PLL methods to be used when the number n of objects being clustered is large, we describe an efficient PLL algorithm that operates in O(N2 log n) time and O(n2) space.

关键词： algorithm complexity algorithm design Integer link linkage clustering method Monotone invariance SAHN clustering method Space distortion

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Fixed-point algorithms for solving the critical value and upper tail quantile of Kuiper's statistics

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HELIYON 2024年第7期10卷 e28274页

作者： Zhang, Hong-Yan Sun, Wei Chen, Xiao Lin, Rui-Jia Zhou, Yu Hainan Normal Univ Sch Informat Sci & Technol 99 Rd LongKun South Haikou 571158 Peoples R China Xiamen Haicang Yunjing Expt Middle Sch 99 Rd Yunmei Xiamen 361026 Peoples R China Hainan Normal Univ Informat Network & Data Ctr 99 Rd LongKun South Haikou 571158 Peoples R China

Kuiper's statistic is a good measure for the difference of ideal distribution and empirical distribution in the goodness -of -fit test. However, it is a challenging problem to solve the critical value and upper tail quantile, or simply Kuiper pair, of Kuiper's statistics due to the difficulties of solving the nonlinear equation and reasonable approximation of infinite series. In this work, the contributions lie in three perspectives: firstly, the second order approximation for the infinite series of the cumulative distribution of the critical value is used to achieve higher precision;secondly, the principles and fixed-point algorithms for solving the Kuiper pair are presented with details;finally, finally, a mistake about the critical value c n a for ( a, n ) = (0 . 01 , 30) in Kuiper's distribution table has been labeled and corrected where n is the sample capacity and a is the upper tail quantile. The algorithms are verified and validated by comparing with the table provided by Kuiper. The methods and algorithms proposed are enlightening and worth of introducing to the college students, computer programmers, engineers, experimental psychologists and so on.

关键词： Kuiper's statistic Upper tail quantile Fixed-point algorithm Numerical approximation algorithm design STEM education

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Cache placement in sensor networks under an update cost constraint

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JOURNAL OF DISCRETE algorithmS 2007年第3期5卷 422-435页

作者： Tang, Bin Gupta, Himanshu SUNY Stony Brook Dept Comp Sci Stony Brook NY 11790 USA

In this paper, we address an optimization problem that arises in the context of cache placement in sensor networks. In particular, we consider the cache placement problem where the goal is to determine a set of nodes in the network to cache/store the given data item, such that the overall communication cost incurred in accessing the item is minimized, under the constraint that the total communication cost in updating the selected caches is less than a given constant. In our network model, there is a single server (containing the original copy of the data item) and multiple client nodes (that wish to access the data item). For various settings of the problem, we design optimal, near-optimal, heuristic-based, and distributed algorithms, and evaluate their performance through simulations on randomly generated sensor networks. (C) 2006 Published by Elsevier B.V.

关键词： Cache placement problem algorithm design Approximation algorithm Sensor networks

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An Efficient algorithm for Sorting and Duplicate Elimination by Using Logarithmic Prime Numbers

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BIG DATA AND COGNITIVE COMPUTING 2024年第9期8卷 96-96页

作者： Yeh, Wei-Chang Forghani-elahabad, Majid Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300044 Taiwan Fed Univ ABC Ctr Math Comp & Cognit BR-09280560 Santo Andre SP Brazil

Data structures such as sets, lists, and arrays are fundamental in mathematics and computer science, playing a crucial role in numerous real-life applications. These structures represent a variety of entities, including solutions, conditions, and objectives. In scenarios involving large datasets, eliminating duplicate elements is essential to reduce complexity and enhance performance. This paper introduces a novel algorithm that uses logarithmic prime numbers to efficiently sort data structures and remove duplicates. The algorithm is mathematically rigorous, ensuring correctness and providing a thorough analysis of its time complexity. To demonstrate its practicality and effectiveness, we compare our method with existing algorithms, highlighting its superior speed and accuracy. An extensive experimental analysis across one thousand random test problems shows that our approach significantly outperforms two alternative techniques from the literature. By discussing the potential applications of the proposed algorithm in various domains, including computer science, engineering, and data management, we illustrate its adaptability through two practical examples in which our algorithm solves the problem more than 3x104 and 7x104 times faster than the existing algorithms in the literature. The results of these examples demonstrate that the superiority of our algorithm becomes increasingly pronounced with larger problem sizes.

关键词： efficient data sorting duplicate elimination logarithmic prime numbers algorithm design time complexity

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Towards explicit representation of an artificial neural network model: Comparison of two artificial neural network rule extraction approaches

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Petroleum 2020年第4期6卷 329-339页

作者： Veronica K.H.Chan Christine W.Chan Energy Informatics Laboratory Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada

In the quest for interpretable models,two versions of a neural network rule extraction algorithm were proposed and *** two algorithms are called the Piece-Wise Linear Artificial Neural Network(PWL-ANN)and enhanced Piece-Wise Linear Artificial Neural Network(enhanced PWL-ANN)*** PWL-ANN algorithm is a decomposition artificial neural network(ANN)rule extraction algorithm,and the enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm and extracts multiple linear regression equations from a trained ANN model by approximating the hidden sigmoid activation functions using N-piece linear *** doing so,the algorithm provides interpretable models from the originally trained opaque ANN models.A detailed application case study illustrates how the generated enhanced-PWL-ANN models can provide understandable IF-THEN rules about a problem *** of the results generated by the two versions of the PWL-ANN algorithm showed that in comparison to the PWL-ANN models,the enhanced-PWL-ANN models support improved fidelities to the originally trained ANN *** results also showed that more concise rule sets could be generated using the enhanced-PWL-ANN *** a more simplified set of rules is desired,the enhanced-PWL-ANN algorithm can be combined with the decision tree *** application of the algorithms to domains related to petroleum engineering can help enhance understanding of the problems.

关键词： Artificial neural networks Rule extraction Regression problem algorithm design

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On Automatic Formation of Effective Therapy Groups in Social Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2018年第3期5卷 713-726页

作者： Hsu, Bay-Yuan Lan, Yi-Feng Shen, Chih-Ya Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Tunghai Univ Ctr Gen Educ Taichung 40704 Taiwan Tunghai Univ Dept Social Work Taichung 40704 Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30013 Taiwan

Group therapy is one of the major clinical interventions to help patients with mental disorders. However, finding an effective therapy group is very challenging, since there are three crucial criteria that should be carefully considered simultaneously: 1) avoidance of isolation and loneliness;2) unfamiliarity of patients;and 3) size of the therapy group. Manual selection of therapy group members, which is the common approach for most psychiatrists and mental health professionals today, may incur human bias and is very time-consuming. Therefore, we propose a new research problem, namely, Unfamiliarity-Aware Therapy Group Selection with Noah's Ark Principle (UTNA) for automatic selections of therapy group members from the social network while addressing the three crucial criteria mentioned above. In this paper, we first analyze the NP-hardness and inapproximability of the UTNA problem. Then, we consider a special case of UTNA on trees, namely, Tree-based UTNA, and propose a linear-time dynamic programming algorithm to find the optimal solution. We then design an efficient algorithm, Effective Therapy Group Discovery, to select suitable members efficiently and effectively for the general UTNA problem. We invite 10 psychiatrists and clinical psychologists to conduct an expert study for validating our problem formulation, where all experts agree that our problem formulation is helpful for them, and the groups selected by our proposed approach are better than or similar to their manual configurations. In addition, we also conduct extensive experiments on three real data sets. Experimental results show that our proposed algorithms outperform other baselines in both efficiency and solution quality.

关键词： algorithm design group formation Noah's ark principle social networks

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FINITE-ELEMENT ANALYSIS ON A PC

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IEEE SOFTWARE 1991年第5期8卷 50-57页

作者： FLOWER, J KOLAWA, A LIANG, T WEINGARTEN, V PARASOFT CORP GIGABIT PROJECTPASADENACA 91107 UNIV SO CALIF ENGNLOS ANGELESCA 90089

The authors describe how they achieved mainframe performance by coding the Cosmos/M finite-element analysis system, a commercial product, for a PC enhanced with a parallel-processing board. The discussion covers design goals, algorithm design, implementation and debugging, performance optimization, and scalability. They focus on financial, algorithmic, and numerical issues, with almost no reference to the numerical principles involved in solving stress-analysis equations.

关键词： design target Mainframes Writers Scalability Finite element method Debugging Numerical Parallel Lines Stress analysis algorithm design

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