"Individually-guided Evolutionary Algorithm for Solving Multi-task Opti" by Xiao Ling Wang, Qi Kang et al.
 

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

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