Abstract
The large-scale optimization problems (LSOPs) have been a hot research in evolutionary computation (EC) community. Although there have been many contributions from researchers in solving LSOPs, the large search space and the numerous local optimal solutions of LSOPs are still two important challenges. In order to alleviate the above challenges, this paper proposes a dimension-based elite learning particle swarm optimizer (DELPSO). In DELPSO, individuals in the population have their unique update probabilities and select specific learning exemplars according to their own properties, making the evolution of the population more efficient. Meanwhile, in the evolutionary process, each individual chooses two different learning exemplars for each dimension, so that each individual can learn from multiple learning exemplars and using the information from multiple individuals to help its own evolution and enhance the diversity. To testify the effectiveness of the proposed algorithm, DELPSO and some large-scale algorithms are experimented on a widely used large-scale benchmark suite IEEE 2013 and the experimental results show that DELPSO outperforms other comparative algorithms in general.
This work was supported in part by the National Natural Science Foundations of China (NSFC) under Grants 62106055, in part by the Guangdong Natural Science Foundation under Grants 2022A1515011825, in part by the Guangzhou Science and Technology Planning Project under Grants 2023A04J0388 and 2023A03J0662.
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Liu, S., Wang, ZJ., Chen, ZG. (2024). A Dimension-Based Elite Learning Particle Swarm Optimizer for Large-Scale Optimization. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2013. Springer, Singapore. https://doi.org/10.1007/978-981-99-9640-7_12
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