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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
S. Qi, R. Wang, T. Zhang, X. Yang, R. Sun, and L. Wang, “A two-layer encoding learning swarm optimizer based on frequent itemsets for sparse large-scale multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1–16, Jun. 2024. doi: 10.1109/JAS.2024.124341
Citation: S. Qi, R. Wang, T. Zhang, X. Yang, R. Sun, and L. Wang, “A two-layer encoding learning swarm optimizer based on frequent itemsets for sparse large-scale multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1–16, Jun. 2024. doi: 10.1109/JAS.2024.124341
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A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

doi: 10.1109/JAS.2024.124341
Funds:  This work was supported by the Scientific Research Project of Xiang Jiang Lab (22XJ02003), the University Fundamental Research Fund (23-ZZCX-JDZ-28), the National Science Fund for Outstanding Young Scholars (62122093), the National Natural Science Foundation of China (72071205), the science and technology innovation Program of Hunan Province (ZC23112101-10), and the Hunan Natural Science Foundation Regional Joint Project (2023JJ50490)
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    Abstract
  • Traditional large-scale multi-objective optimization algorithms (LSMOEAs) encounter difficulties when dealing with sparse large-scale multi-objective optimization problems (SLMOPs) where most decision variables are zero. As a result, many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately. Nevertheless, existing optimizers often focus on locating non-zero variable positions to optimize the binary variables Mask. However, approximating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized. In data mining, it is common to mine frequent itemsets appearing together in a dataset to reveal the correlation between data. Inspired by this, we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets (TELSO) to address these SLMOPs. TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence. Experimental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms (SLMOEAs) in terms of performance and convergence speed.

     

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