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A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles

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  • Jeng-Shyang Pan

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    Department of Information Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung 41349, Taiwan)

  • Li-Fa Liu

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shu-Chuan Chu

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia)

  • Pei-Cheng Song

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Geng-Geng Liu

    (College of Computer and Data Science, Fuzhou University, Xueyuan Road No.2, Fuzhou 350116, China)

Abstract

Heuristic optimization algorithms have been proved to be powerful in solving nonlinear and complex optimization problems; therefore, many effective optimization algorithms have been applied to solve optimization problems in real-world scenarios. This paper presents a modification of the recently proposed Gaining–Sharing Knowledge (GSK)-based algorithm and applies it to optimize resource scheduling in the Internet of Vehicles (IoV). The GSK algorithm simulates different phases of human life in gaining and sharing knowledge, which is mainly divided into the senior phase and the junior phase. The individual is initially in the junior phase in all dimensions and gradually moves into the senior phase as the individual interacts with the surrounding environment. The main idea used to improve the GSK algorithm is to divide the initial population into different groups, each searching independently and communicating according to two main strategies. Opposite-based learning is introduced to correct the direction of convergence and improve the speed of convergence. This paper proposes an improved algorithm, named parallel opposition-based Gaining–Sharing Knowledge-based algorithm (POGSK). The improved algorithm is tested with the original algorithm and several classical algorithms under the CEC2017 test suite. The results show that the improved algorithm significantly improves the performance of the original algorithm. When POGSK was applied to optimize resource scheduling in IoV, the results also showed that POGSK is more competitive.

Suggested Citation

  • Jeng-Shyang Pan & Li-Fa Liu & Shu-Chuan Chu & Pei-Cheng Song & Geng-Geng Liu, 2023. "A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2953-:d:1185366
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    References listed on IDEAS

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    1. Jiang, Shi-Jie & Chu, Shu-Chuan & Zou, Fu-Min & Shan, Jie & Zheng, Shi-Guang & Pan, Jeng-Shyang, 2023. "A parallel Archimedes optimization algorithm based on Taguchi method for application in the control of variable pitch wind turbine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 306-327.
    2. Junping Yao & Shaojian Qu, 2022. "Research on Optimization Algorithm for Resource Allocation of Heterogeneous Car Networking Engineering Cloud System Based on Big Data," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-6, March.
    3. Pan, Jeng-Shyang & Zhang, Li-Gang & Wang, Ruo-Bin & Snášel, Václav & Chu, Shu-Chuan, 2022. "Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 343-373.
    Full references (including those not matched with items on IDEAS)

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