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  • Open Access


    Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

    Fatma S. Alrayes1, Najm Alotaibi2, Jaber S. Alzahrani3, Sana Alazwari4, Areej Alhogail5, Ali M. Al-Sharafi6, Mahmoud Othman7, Manar Ahmed Hamza8,*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3037-3052, 2023, DOI:10.32604/csse.2023.033970

    Abstract Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla… More >

  • Open Access


    Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

    R. Rengaraj alias Muralidharan1,*, K. Latha2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1923-1937, 2023, DOI:10.32604/iasc.2023.029495

    Abstract In cloud computing (CC), resources are allocated and offered to the clients transparently in an on-demand way. Failures can happen in CC environment and the cloud resources are adaptable to fluctuations in the performance delivery. Task execution failure becomes common in the CC environment. Therefore, fault-tolerant scheduling techniques in CC environment are essential for handling performance differences, resource fluxes, and failures. Recently, several intelligent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics. With this motivation, this study focuses on the design of Gorilla Troops Optimizer Based… More >

  • Open Access


    An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

    Yaning Xiao, Xue Sun*, Yanling Guo, Sanping Li, Yapeng Zhang, Yangwei Wang

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 815-850, 2022, DOI:10.32604/cmes.2022.019198

    Abstract Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting… More >

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