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Transfer learning based evolutionary algorithm framework for multi-objective optimization problems

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Abstract

In this paper, a transfer learning based evolutionary algorithm (TLEA) framework for multi-objective optimization problems (MOPs) is proposed. In the TLEA framework, a complex multi-objective optimization task is decomposed into a set of relatively simple multi-objective optimization subtasks and then optimized collaboratively by parallel subpopulation searches with the proposed transfer learning method. More specifically, neighboring subtasks may have some similar features during parallel searches of corresponding subpopulations, and those similarities can be exploited through the proposed transfer learning strategy to improve the collaboration among these search subpopulations and achieve greater efficiency. To show the generality of the proposed algorithm framework, two implementations of the proposed TLEA framework based on differential evolution (DE) and particle swarm optimization (PSO), i.e., TLPSO and TLDE, are presented and studied in detail. In TLPSO and TLDE, the subproblem features are reflected by the search subpopulations, which are generated by a pair of specific parameters. Therefore, subpopulations can adaptively adjust parameter settings by learning useful information from neighboring subproblems with more appropriate parameters during the search. The experimental results show that TLPSO performs better than other algorithms on at least five out of 12 test problems in terms of the IGD indicator and on at least seven out of 12 test problems in terms of the HV indicator. TLDE has an advantage over the other algorithms on five out of 12 test problems in terms of the IGD indicator and on seven out of 12 test problems in terms of the HV indicator.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (62006044, 62172110), in part by the Natural Science Foundation of Guangdong Province (2022A1515010130), and in part by the Programme of Science and Technology of Guangdong Province (2021A0505110004).

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    1. School of Mathematics and Statistics, Guangdong University of Technology, YingLo Road, Guangzhou, 510520, Guangdong, China

      Jiaheng Huang, Jiechang Wen, Lei Chen & Hai-Lin Liu

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    Huang, J., Wen, J., Chen, L. et al. Transfer learning based evolutionary algorithm framework for multi-objective optimization problems. Appl Intell 53, 18085–18104 (2023). https://doi.org/10.1007/s10489-022-04444-w

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