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

Jeng-Shyang Pan, Li-Fa Liu, Shu-Chuan Chu (), Pei-Cheng Song and Geng-Geng Liu
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Jeng-Shyang Pan: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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
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

Mathematics, 2023, vol. 11, issue 13, 1-25

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.

Keywords: Gaining–Sharing Knowledge-based algorithm; Taguchi method; opposition-based learning; resource scheduling; parallel mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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