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  • Open Access


    Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

    Hang Yang1,2,*, Xunbo Li1, Witold Pedrycz2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1531-1551, 2023, DOI:10.32604/iasc.2023.039256

    Abstract Energy supply is one of the most critical challenges of wireless sensor networks (WSNs) and industrial wireless sensor networks (IWSNs). While research on coverage optimization problem (COP) centers on the network’s monitoring coverage, this research focuses on the power banks’ energy supply coverage. The study of 2-D and 3-D spaces is typical in IWSN, with the realistic environment being more complex with obstacles (i.e., machines). A 3-D surface is the field of interest (FOI) in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN. The hybrid… More >

  • Open Access


    A Client Selection Method Based on Loss Function Optimization for Federated Learning

    Yan Zeng1,2,3, Siyuan Teng1, Tian Xiang4,*, Jilin Zhang1,2,3, Yuankai Mu5, Yongjian Ren1,2,3,*, Jian Wan1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1047-1064, 2023, DOI:10.32604/cmes.2023.027226

    Abstract Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the… More > Graphic Abstract

    A Client Selection Method Based on Loss Function Optimization for Federated Learning

  • Open Access


    Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

    Ali E. Takieldeen1, El-Sayed M. El-kenawy1,2, Mohammed Hadwan3,4,5,*, Rokaia M. Zaki6,7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1465-1481, 2022, DOI:10.32604/cmc.2022.026026

    Abstract Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed More >

  • Open Access


    Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

    Qamar Abbas1, Khalid Mahmood Malik2, Abdul Khader Jilani Saudagar3,*, Muhammad Badruddin Khan3, Mozaherul Hoque Abul Hasanat3, Abdullah AlTameem3, Mohammed AlKhathami3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211

    Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm… More >

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