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  • Open Access


    New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification

    Yu Xue1,2,* , Yan Zhao1

    Journal on Internet of Things, Vol.3, No.3, pp. 109-118, 2021, DOI:10.32604/jiot.2021.014980

    Abstract As a new intelligent optimization method, brain storm optimization (BSO) algorithm has been widely concerned for its advantages in solving classical optimization problems. Recently, an evolutionary classification optimization model based on BSO algorithm has been proposed, which proves its effectiveness in solving the classification problem. However, BSO algorithm also has defects. For example, large-scale datasets make the structure of the model complex, which affects its classification performance. In addition, in the process of optimization, the information of the dominant solution cannot be well preserved in BSO, which leads to its limitations in classification performance. Moreover, its generation strategy is inefficient… More >

  • Open Access


    OBSO Based Fractional PID for MPPT-Pitch Control of Wind Turbine Systems

    Ibrahim M. Mehedi1,2,*, Ubaid M. Al-Saggaf1,2, Mahendiran T. Vellingiri1, Ahmad H. Milyani1, Nordin Bin Saad3, Nor Zaihar Bin Yahaya3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4001-4017, 2022, DOI:10.32604/cmc.2022.021981

    Abstract In recent times, wind energy receives maximum attention and has become a significant green energy source globally. The wind turbine (WT) entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and grid. The turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power extraction. The pitch control angle is employed to effectively operate the WT at the above nominal wind speed. Besides, the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep… More >

  • Open Access


    Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

    Nebojsa Bacanin1, Khaled Alhazmi2,*, Miodrag Zivkovic1, K. Venkatachalam3, Timea Bezdan1, Jamel Nebhen4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4199-4215, 2022, DOI:10.32604/cmc.2022.020449

    Abstract In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing… More >

  • Open Access


    Brain Storm Optimization Based Clustering for Learning Behavior Analysis

    Yu Xue1,2,*, Jiafeng Qin1, Shoubao Su2, Adam Slowik3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 211-219, 2021, DOI:10.32604/csse.2021.016693

    Abstract Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students’ learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data. This study proposes a… More >

  • Open Access


    A Clustering Method Based on Brain Storm Optimization Algorithm

    Tianyu Wang, Yu Xue, Yan Zhao, Yuxiang Wang*, Yan Zhang, Yuxiang He

    Journal of Information Hiding and Privacy Protection, Vol.2, No.3, pp. 135-142, 2020, DOI:10.32604/jihpp.2020.010362

    Abstract In the field of data mining and machine learning, clustering is a typical issue which has been widely studied by many researchers, and lots of effective algorithms have been proposed, including K-means, fuzzy c-means (FCM) and DBSCAN. However, the traditional clustering methods are easily trapped into local optimum. Thus, many evolutionary-based clustering methods have been investigated. Considering the effectiveness of brain storm optimization (BSO) in increasing the diversity while the diversity optimization is performed, in this paper, we propose a new clustering model based on BSO to use the global ability of BSO. In our experiment, we apply the novel… More >

  • Open Access


    Wiener Model Identification Using a Modified Brain Storm Optimization Algorithm

    Tianhong Pan1,*, Ying Song2, Shan Chen2

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 934-946, 2020, DOI:10.32604/iasc.2020.010125

    Abstract The Wiener model is widely used in industrial processes. It is composed of a linear dynamic block and a nonlinear static block. Estimating the Wiener model is challenging because of the diversity of static nonlinear functions and the immeasurableness of intermediate signals owing to the series structure of the Wiener model. Existing optimization algorithms cannot satisfy the requirements of accuracy and efficiency of identification and often lose into a local optimum. Herein, a modified Brain Storm Optimization (mBSO) is proposed to estimate the parameters of the Wiener model. Many different combinations of individuals from intra or extra-groups ensure the diversity… More >

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