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  • 1.
    Fan, Qingfeng
    et al.
    Laboratoire DAVID, University of Versailles-Saint-Quentin.
    Xiong, Naixue
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Department of Business and Computer Science, Southwestern Oklahoma State University.
    Zeitouni, Karine
    Laboratoire DAVID, University of Versailles-Saint-Quentin.
    Wu, Qiongli
    Laboratory Applied Mathematics and Systems, Ecole Centrale de Paris.
    Tian, Yu-Chu
    School of Electrical Engineering and Computer Science, Queensland University of Technology.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Game Balanced Multi-factor Multicast Routing in Sensor Grid Networks2016Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 367-368, s. 550-572Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In increasingly important sensor grid networks, multicast routing is widely used in date aggregation and distributed query processing. It requires multicast trees for efficient data transmissions. However, sensor nodes in such networks typically have limited resources and computing power. Efforts have been made to consider the space, energy and data factors separately to optimize the network performance. Considering these factors simultaneously, this paper presents a game balance based multi-factor multicast routing approach for sensor grid networks. It integrates the three factors into a unified model through a linear combination. The model is standardized and then solved theoretically by using the concept of game balance from game theory. The solution gives Nash equilibrium, implying a well balanced result for all the three factors. The theoretic results are implemented in algorithms for cluster formation, cluster core selection, cluster tree construction, and multicast routing. Extensive simulation experiments show that the presented approach gives mostly better overall performance than benchmark methods

  • 2.
    Janzura, M.
    et al.
    Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic.
    Koski, Timo
    Luleå tekniska universitet.
    Otahal, A.
    Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic.
    Minimum entropy of error principle in estimation1994Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 79, nr 1-2, s. 123-144Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The principle of minimum error entropy estimation as found in the work of Weidemann and Stear is reformulated as a problem of finding optimum locations of probability densities in a given mixture such that the resulting (differential) entropy is minimized. New results concerning the entropy lower bound are derived. Continuity of the entropy and attaining the minimum entropy are proved in the case where the mixture is finite. Some other examples and situations, in particular that of symmetric unimodal densities, are studied in more detail

  • 3.
    Koski, Timo
    et al.
    Luleå tekniska universitet.
    Persson, Lars-Erik
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Matematiska vetenskaper.
    Some properties of generalized exponential entropies with applications to data compression1992Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 62, nr 1, s. 103-132Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    I. Csiszár discussed generalized entropies in his lecture at the Sixth Prague Conference on Information Theory. The authors emphasize that Csiszár noted the link between certain lower bounds for the quantization error and Rényi's differential entropy of order $\alpha$. Another important reference is the paper by L. L. Campbell where the concept of an exponential entropy was introduced. The authors investigate "several consequences that are of interest in the theory of data (or signal) compression". They also "investigate especially the exponential families of distributions, in particular the Miller-Thomas (or generalized Gaussian) family of distributions". The paper is a detailed discussion of the aforementioned problems coupled with examples and details of the possible applications. Exponential entropy is calculated for the uniform distribution, the univariate Gaussian distribution, the Laplace distribution, the Miller-Thomas distribution, an infinite-dimensional Gaussian exponential family, the Gauss-Laplace mixture and the multivariate Gaussian distribution. The extent of a distribution is given for the shape parameter in the Miller-Thomas distribution. Campbell's representation for E$[\alpha, 1 ; f]$ and the connection between an entropy series and data compression are discussed. A lower bound for the entropy of a partition (as defined in the paper) is given. Examples and proofs are illustrated with outputs from Mathematica.

  • 4.
    Marinakis, Y.
    et al.
    Technical University of Crete, School of Production Engineering and Management, Chania, Greece.
    Marinaki, M.
    Technical University of Crete, School of Production Engineering and Management, Chania, Greece.
    Migdalas, Athanasios
    Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi. Aristotle University of Thessalonike, Department of Civil Engineering, Thessalonike, Greece.
    A Multi-Adaptive Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows2019Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 481, s. 311-329Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the Vehicle Routing Problem with Time Windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature.

  • 5.
    Wei, Wei
    et al.
    School of Computer Science and Engineering, Xi’an University of Technology.
    Song, Houbing
    Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV .
    Li, Wei
    Centre for Distributed and High Performance Computing, School of Information Technologies, The University of Sydney.
    Shen, Peiyi
    National school of Software, Xidian University.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network2017Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 408, s. 100-114Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Wireless sensor networks can support building and transportation system automation in numerous ways. An emerging application is to guide drivers to promptly locate vacant parking spaces in large parking structures during peak hours. This paper proposes efficient parking navigation via a continuous information potential field and gradient ascent method. Our theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation. The empirical study demonstrates that the proposed algorithm performs more efficiently than existing algorithms.

  • 6.
    Xia, Zhihua
    et al.
    Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, College of Computer and Software, Nanjing University of Information Science & Technology.
    Xiong, Neal N.
    Department of Business and Computer Science, Southwestern Oklahoma State University.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Sun, Xingming
    Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, College of Computer and Software, Nanjing University of Information Science & Technology.
    EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing2017Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 387, s. 195-204Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The content-based image retrieval (CBIR) has been widely studied along with the increasing importance of images in our daily life. Compared with the text documents, images consume much more storage and thus are very suitable to be stored on the cloud servers. The outsourcing of CBIR to the cloud servers can be a very typical service in cloud computing. For the privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before being outsourced, which will cause the CBIR technologies in plaintext domain unusable. In this paper, we propose a scheme that supports CBIR over the encrypted images without revealing the sensitive information to the cloud server. Firstly, the feature vectors are extracted to represent the corresponding images. Then, the pre-filter tables are constructed with the locality-sensitive hashing to increase the search efficiency. Next, the feature vectors are protected by the secure k-nearest neighbor (kNN) algorithm. The security analysis and experiments show the security and efficiency of the proposed scheme.

  • 7.
    Yi,, J.-H.
    et al.
    School of Mathematics and Big Data, Foshan University, Foshan, China; School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.
    Xing, L. -N.
    School of Mathematics and Big Data, Foshan University, Foshan, China.
    Wang, G.-G.
    Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
    Dong, J.
    Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Alavi, A.H.
    Department of Civil and Environmental Engineering, University of Missouri, Columbia, United States.
    Wang, L.
    Department of Automation, Tsinghua University, Beijing, China.
    Behavior of crossover operators in NSGA-III for large-scale optimization problems2020Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 509, s. 470-487Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

  • 8.
    Zhang, Qingke
    et al.
    School of Computer Science and technology, Engineering Research Center of Digital Media Technology, Ministry of Education, Shandong University, Jinan.
    Liu, Weiguo
    School of Computer Science and technology, Engineering Research Center of Digital Media Technology, Ministry of Education, Shandong University, Jinan.
    Men, Xiangxu
    School of Computer Science and technology, Engineering Research Center of Digital Media Technology, Ministry of Education, Shandong University, Jinan.
    Jiang, Bo
    Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Vector coevolving particle swarm optimization algorithm2017Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 394-395, s. 273-298Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results.

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