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  • Open Access

    ARTICLE

    A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

    Weizhi Liao1, Xiaoyun Xia1,3, Xiaojun Jia1, Shigen Shen2,*, Helin Zhuang4,*, Xianchao Zhang1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3297-3323, 2023, DOI:10.32604/cmc.2023.040967 - 08 October 2023

    Abstract As a new bionic algorithm, Spider Monkey Optimization (SMO) has been widely used in various complex optimization problems in recent years. However, the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant. Thus, this paper focuses on how to reconstruct SMO to improve its performance, and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design (SMO3) is developed. A position updating method based on the historical optimal domain and particle swarm for Local Leader Phase (LLP) and Global Leader Phase (GLP) is… More >

  • Open Access

    ARTICLE

    An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

    Hui Xu, Yalin Hu*, Weidong Cao, Longjie Han

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.039227 - 08 October 2023

    Abstract The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimization-seeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the… More >

  • Open Access

    ARTICLE

    Improved Shark Smell Optimization Algorithm for Human Action Recognition

    Inzamam Mashood Nasir1,*, Mudassar Raza1, Jamal Hussain Shah1, Muhammad Attique Khan2, Yun-Cheol Nam3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2667-2684, 2023, DOI:10.32604/cmc.2023.035214 - 08 October 2023

    Abstract Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) models had been widely used and achieved impressive results through feature fusion. The accuracy and effectiveness of these models continue to be the biggest challenge in this field. In this article, a novel feature optimization algorithm, called improved Shark Smell Optimization (iSSO) is proposed to reduce the redundancy of extracted… More >

  • Open Access

    ARTICLE

    Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2, Miguel Rio1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2767-2782, 2023, DOI:10.32604/iasc.2023.039440 - 11 September 2023

    Abstract The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across… More >

  • Open Access

    ARTICLE

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

    Tiantian Liang*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331 - 07 September 2023

    Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga… More > Graphic Abstract

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

  • Open Access

    ARTICLE

    Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

    Tamilvizhi Thanarajan1, Youseef Alotaibi2, Surendran Rajendran3,*, Krishnaraj Nagappan4

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1995-2013, 2023, DOI:10.32604/cmc.2023.039644 - 30 August 2023

    Abstract Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to… More >

  • Open Access

    ARTICLE

    Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

    Ali S. Alghamdi1, Mohana Alanazi2, Abdulaziz Alanazi3, Yazeed Qasaymeh1,*, Muhammad Zubair1,4, Ahmed Bilal Awan5, M. G. B. Ashiq6

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2163-2192, 2023, DOI:10.32604/cmes.2023.029453 - 03 August 2023

    Abstract To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat… More >

  • Open Access

    ARTICLE

    Decision Analysis on IoV Routing Transmission and Energy Efficiency Optimization Algorithm with AmBC

    Baofeng Ji1,2,3,*, Mingkun Zhang1,2, Weixing Wang1, Song Chen4

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2661-2673, 2023, DOI:10.32604/cmes.2023.028762 - 03 August 2023

    Abstract The improvement of the quality and efficiency of vehicle wireless network data transmission is always a key concern in the Internet of Vehicles (IoV). Routing transmission solved the limitation of transmission distance to a certain extent. Traditional routing algorithm cannot adapt to complex traffic environment, resulting in low transmission efficiency. In order to improve the transmission success rate and quality of vehicle network routing transmission, make the routing algorithm more suitable for complex traffic environment, and reduce transmission power consumption to improve energy efficiency, a comprehensive optimized routing transmission algorithm is proposed. Based on the… More >

  • Open Access

    ARTICLE

    A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments

    Popuri Srinivasarao, Aravapalli Rama Satish*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1527-1547, 2023, DOI:10.32604/csse.2023.038951 - 28 July 2023

    Abstract Responding to complex analytical queries in the data warehouse (DW) is one of the most challenging tasks that require prompt attention. The problem of materialized view (MV) selection relies on selecting the most optimal views that can respond to more queries simultaneously. This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs. The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique (ECHT). The constraints such as self-adaptive… More >

  • Open Access

    ARTICLE

    Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

    Zhenyu Yan1,*, Haotian Bian2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2241-2257, 2023, DOI:10.32604/csse.2023.040603 - 28 July 2023

    Abstract As millimeter waves will be widely used in the Internet of Things (IoT) and Telematics to provide high bandwidth communication and mass connectivity, the coverage optimization of base stations can effectively improve the quality of communication services. How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers. This paper proposes the Muti-Fusion Sparrow Search Algorithm (MFSSA) optimize the situation to address the problem of discrete coverage maximization and rapid convergence. Firstly, the initial swarm diversity is enriched using a sine chaotic map, and dynamic adaptive weighting is… More >

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