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A non-revisiting framework for evolutionary multi-task optimization

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Abstract

Multi-task optimization is an emerging research topic in evolutionary computation, which aims to solve multiple optimization tasks simultaneously through knowledge transfer. However, existing multi-task evolutionary algorithms suffer from the re-evaluation problem, leading to unnecessary consumption of computing resources. To address this issue, a non-revisiting framework is proposed, which allows the non-revisiting scheme to be aided by historical information during the evolutionary search process. Moreover, an individual updating strategy is designed to improve the search efficiency of the algorithm and enhance the ability to escape local optima. Furthermore, a parallel scheme of the proposed framework is developedNational Frontiers Science Center for Industrial Intelligence and Systems Optimizationcomputation time on the CUDA architecture. To evaluate the effectiveness of the proposed framework, it is integrated with success-history based adaptive differential evolution. A comparative study of the proposed algorithm with eight state-of-the-art multi-task evolutionary algorithms is performed on nine benchmark problems. The experimental results demonstrate that the proposed algorithm outperforms the existing algorithms, highlighting its potential for solving multi-task optimization problems.

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Notes

  1. The friedman test is implemented by the KEEL Software Tool available at https://sci2s.ugr.es/keel/.

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Acknowledgements

This study received support from the Central Government Guided Local Science and Technology Development Fund Project (1653137155953), the General Project of the Liaoning Provincial Department of Education (LJKMZ20220613), the Ningxia Natural Science Foundation Project (2023AAC03361), and the 111 Project (B16009).

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Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, 110169, People’s Republic of China

    Yufei Yang & Changsheng Zhang

  2. National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, 110169, People’s Republic of China

    Changsheng Zhang

  3. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang, 110169, People’s Republic of China

    Bin Zhang

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  1. Yufei Yang
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  2. Changsheng Zhang
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Contributions

Yufei Yang: Methodology, Software, Validation, Formal analysis, Data curation, Writing-Original Draft, Visualization, Investigation. Changsheng Zhang: Methodology, Software, Validation, Formal analysis, Data curation, Writing Reviewing and Editing, Visualization. Bin Zhang: Conceptualization, Investigation, Resources, Supervision, Project administration, Funding acquisition.

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Correspondence to Changsheng Zhang.

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Appendix A   Revisits in MTEAs and STEAs

Appendix A   Revisits in MTEAs and STEAs

The paper investigates the performance of two algorithms, GA and MFEA-II, on the CEC2017 multi-task benchmark test set. The study is conducted under identical experimental conditions as presented in Section 4 to illustrate the difference between revisits in STEAs and MTEAs. The convergence curves of GA and MFEA-II for problems 1 to 9 are presented in Fig. 10, Fig. 11, and Fig. 12.

MFEA-II outperforms GA in terms of producing better final results due to knowledge transfer and consistently converging earlier than GA. However, the study finds that MFEA-II explores specific local regions faster, resulting in repeated explorations of previously searched regions as the iteration progresses. This phenomenon wastes computational resources despite the aim to optimize results. The study shows that this phenomenon occurs more frequently and earlier in MTOPs than in single-task optimization (STO) due to the positive knowledge transfer effect.

Fig. 10
figure 10

The convergences of GA and MFEAII on Problem 1 to problem 3

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Fig. 11
figure 11

The convergences of GA and MFEAII on Problem 4 to problem 6

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Fig. 12
figure 12

The convergences of GA and MFEAII on Problem 7 to problem 9

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Yang, Y., Zhang, C. & Zhang, B. A non-revisiting framework for evolutionary multi-task optimization. Appl Intell 53, 25931–25953 (2023). https://doi.org/10.1007/s10489-023-04918-5

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