Proceedings of the
The Nineteenth International Conference on Computational Intelligence and Security (CIS 2023)
December 1 – 4, 2023, Haikou, China
A Multitasking Optimization Algorithm based on Multi-Elite Transfer
1Xi'an University of Technology School of Computer Science and Engineering Xi'an, China.
2School of Electronic and Information Engineering, Ankang University, Ankang, China /EADDRESS/3National University of Defense Technology College of Information and Communication Wuhan.
ABSTRACT
Transferring elite solutions between related tasks can facilitate the convergence of a multitasking evolutionary algorithm. However, if the transferred elite solutions are local optimal solutions, it may cause the algorithm to fall into local optimum. To address this problem, this paper proposes a multitasking optimization algorithm (MTO-ME) based on multiple elite solutions transfer. The experimental results on multitasking optimization benchmarks show that the proposed MTO-ME algorithm outperforms other state-of-the-art algorithms in terms of solution accuracy and convergence performance.
Keywords: Evolutionary multitasking, Knowledge transfer, k-means clustering, Multi-elite.
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1Xi'an University of Technology School of Computer Science and Engineering Xi'an, China.
2School of Electronic and Information Engineering, Ankang University, Ankang, China /EADDRESS/3National University of Defense Technology College of Information and Communication Wuhan.
ABSTRACT
Transferring elite solutions between related tasks can facilitate the convergence of a multitasking evolutionary algorithm. However, if the transferred elite solutions are local optimal solutions, it may cause the algorithm to fall into local optimum. To address this problem, this paper proposes a multitasking optimization algorithm (MTO-ME) based on multiple elite solutions transfer. The experimental results on multitasking optimization benchmarks show that the proposed MTO-ME algorithm outperforms other state-of-the-art algorithms in terms of solution accuracy and convergence performance.
Keywords: Evolutionary multitasking, Knowledge transfer, k-means clustering, Multi-elite.
Download PDF