Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants.
Identifier
85146956516 (Scopus)
ISBN
[9781665472432]
Publication Title
Icnsc 2022 Proceedings of 2022 IEEE International Conference on Networking Sensing and Control Autonomous Intelligent Systems
External Full Text Location
https://doi.org/10.1109/ICNSC55942.2022.10004137
Grant
2021-cyxt2-kj10
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Wang, Xiao Ling; Kang, Qi; and Zhou, Meng Chu, "Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems" (2022). Faculty Publications. 3351.
https://digitalcommons.njit.edu/fac_pubs/3351