• Title/Summary/Keyword: Information Algorithm

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A Hybridization of Adaptive Genetic Algorithm and Particle Swarm Optimization for Numerical Optimization Functions

  • Yun, Young-Su;Gen, Mitsuo
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 2008.10b
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    • pp.463-467
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    • 2008
  • Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

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A new Ensemble Clustering Algorithm using a Reconstructed Mapping Coefficient

  • Cao, Tuoqia;Chang, Dongxia;Zhao, Yao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.7
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    • pp.2957-2980
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    • 2020
  • Ensemble clustering commonly integrates multiple basic partitions to obtain a more accurate clustering result than a single partition. Specifically, it exists an inevitable problem that the incomplete transformation from the original space to the integrated space. In this paper, a novel ensemble clustering algorithm using a newly reconstructed mapping coefficient (ECRMC) is proposed. In the algorithm, a newly reconstructed mapping coefficient between objects and micro-clusters is designed based on the principle of increasing information entropy to enhance effective information. This can reduce the information loss in the transformation from micro-clusters to the original space. Then the correlation of the micro-clusters is creatively calculated by the Spearman coefficient. Therefore, the revised co-association graph between objects can be built more accurately because the supplementary information can well ensure the completeness of the whole conversion process. Experiment results demonstrate that the ECRMC clustering algorithm has high performance, effectiveness, and feasibility.

Wireless Network Health Information Retrieval Method Based on Data Mining Algorithm

  • Xiaoguang Guo
    • Journal of Information Processing Systems
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    • v.19 no.2
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    • pp.211-218
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    • 2023
  • In order to improve the low accuracy of traditional wireless network health information retrieval methods, a wireless network health information retrieval method is designed based on data mining algorithm. The invalid health information stored in wireless network is filtered by data mapping, and the health information is clustered by data mining algorithm. On this basis, the high-frequency words of health information are classified to realize wireless network health information retrieval. The experimental results show that exactitude of design way is significantly higher than that of the traditional method, which can solve the problem of low accuracy of the traditional wireless network health information retrieval method.

Differential Evolution with Multi-strategies based Soft Island Model

  • Tan, Xujie;Shin, Seong-Yoon
    • Journal of information and communication convergence engineering
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    • v.17 no.4
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    • pp.261-266
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    • 2019
  • Differential evolution (DE) is an uncomplicated and serviceable developmental algorithm. Nevertheless, its execution depends on strategies and regulating structures. The combination of several strategies between subpopulations helps to stabilize the probing on DE. In this paper, we propose a unique k-mean soft island model DE(KSDE) algorithm which maintains population diversity through soft island model (SIM). A combination of various approaches, called KSDE, intended for migrating the subpopulation information through SIM is developed in this study. First, the population is divided into k subpopulations using the k-means clustering algorithm. Second, the mutation pattern is singled randomly from a strategy pool. Third, the subpopulation information is migrated using SIM. The performance of KSDE was analyzed using 13 benchmark indices and compared with those of high-technology DE variants. The results demonstrate the efficiency and suitability of the KSDE system, and confirm that KSDE is a cost-effective algorithm compared with four other DE algorithms.

Color Image Coding Based on Shape-Adaptive All Phase Biorthogonal Transform

  • Wang, Xiaoyan;Wang, Chengyou;Zhou, Xiao;Yang, Zhiqiang
    • Journal of Information Processing Systems
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    • v.13 no.1
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    • pp.114-127
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    • 2017
  • This paper proposes a color image coding algorithm based on shape-adaptive all phase biorthogonal transform (SA-APBT). This algorithm is implemented through four procedures: color space conversion, image segmentation, shape coding, and texture coding. Region-of-interest (ROI) and background area are obtained by image segmentation. Shape coding uses chain code. The texture coding of the ROI is prior to the background area. SA-APBT and uniform quantization are adopted in texture coding. Compared with the color image coding algorithm based on shape-adaptive discrete cosine transform (SA-DCT) at the same bit rates, experimental results on test color images reveal that the objective quality and subjective effects of the reconstructed images using the proposed algorithm are better, especially at low bit rates. Moreover, the complexity of the proposed algorithm is reduced because of uniform quantization.

Enhanced technique for Arabic handwriting recognition using deep belief network and a morphological algorithm for solving ligature segmentation

  • Essa, Nada;El-Daydamony, Eman;Mohamed, Ahmed Atwan
    • ETRI Journal
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    • v.40 no.6
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    • pp.774-787
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    • 2018
  • Arabic handwriting segmentation and recognition is an area of research that has not yet been fully understood. Dealing with Arabic ligature segmentation, where the Arabic characters are connected and unconstrained naturally, is one of the fundamental problems when dealing with the Arabic script. Arabic character-recognition techniques consider ligatures as new classes in addition to the classes of the Arabic characters. This paper introduces an enhanced technique for Arabic handwriting recognition using the deep belief network (DBN) and a new morphological algorithm for ligature segmentation. There are two main stages for the implementation of this technique. The first stage involves an enhanced technique of the Sari segmentation algorithm, where a new ligature segmentation algorithm is developed. The second stage involves the Arabic character recognition using DBNs and support vector machines (SVMs). The two stages are tested on the IFN/ENIT and HACDB databases, and the results obtained proved the effectiveness of the proposed algorithm compared with other existing systems.

Genetic algorithm-based content distribution strategy for F-RAN architectures

  • Li, Xujie;Wang, Ziya;Sun, Ying;Zhou, Siyuan;Xu, Yanli;Tan, Guoping
    • ETRI Journal
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    • v.41 no.3
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    • pp.348-357
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    • 2019
  • Fog radio access network (F-RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm-based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F-RAN. First, an F-RAN system model is presented that includes a certain number of randomly distributed fog access points (F-APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F-RANs is described. Third, the details of the proposed optimal genetic algorithm-based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.

Tucker Modeling based Kronecker Constrained Block Sparse Algorithm

  • Zhang, Tingping;Fan, Shangang;Li, Yunyi;Gui, Guan;Ji, Yimu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.2
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    • pp.657-667
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    • 2019
  • This paper studies synthetic aperture radar (SAR) imaging problem which the scatterers are often distributed in block sparse pattern. To exploiting the sparse geometrical feature, a Kronecker constrained SAR imaging algorithm is proposed by combining the block sparse characteristics with the multiway sparse reconstruction framework with Tucker modeling. We validate the proposed algorithm via real data and it shows that the our algorithm can achieve better accuracy and convergence than the reference methods even in the demanding environment. Meanwhile, the complexity is smaller than that of the existing methods. The simulation experiments confirmed the effectiveness of the algorithm as well.

Research on Low-energy Adaptive Clustering Hierarchy Protocol based on Multi-objective Coupling Algorithm

  • Li, Wuzhao;Wang, Yechuang;Sun, Youqiang;Mao, Jie
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.4
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    • pp.1437-1459
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    • 2020
  • Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

A SM2 Elliptic Curve Threshold Signature Scheme without a Trusted Center

  • Jie, Yan;Yu, Lu;Li-yun, Chen;Wei, Nie
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.2
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    • pp.897-913
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    • 2016
  • Threshold signature is very important in identity authentication and some other applications. In December 2010, Chinese Encryption Administration released the SM2 elliptic curve digital signature algorithm as the first standard of the digital signature algorithm in China. At present, the papers on the threshold signature scheme based on this algorithm are few. A SM2 elliptic curve threshold signature scheme without a trusted center is proposed according to the Joint-Shamir-RSS algorithm, the Joint-Shamir-ZSS algorithm, the sum or diff-SS algorithm, the Mul-SS algorithm, the Inv-SS algorithm and the PM-SS algorithm. The proposed scheme is analyzed from correctness, security and efficiency. The correctness analysis shows that the proposed scheme can realize the effective threshold signature. The security analysis shows that the proposed scheme can resist some kinds of common attacks. The efficiency analysis shows that if the same secret sharing algorithms are used to design the threshold signature schemes, the SM2 elliptic curve threshold signature scheme will be more efficient than the threshold signature scheme based on ECDSA.