A Dynamic Probability-Controlled Learning Swarm Optimizer for large-scale optimization

doi:10.1016/j.swevo.2026.102424

A Dynamic Probability-Controlled Learning Swarm Optimizer for large-scale optimization

2026-05-27

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DOI：10.1016/j.swevo.2026.102424

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Large-scale optimization problems (LSOPs) pose significant challenges, as their vast search spaces often lead to a trade-off dilemma between effective exploration and efficient exploitation. To address this issue, this paper proposes a Dynamic Probability-Controlled Learning Swarm Optimizer (DPCLSO). DPCLSO introduces two coordinated mechanisms: (1) a probability-controlled learning mechanism, which adaptively regulates particle updates based on individual quality and search stage; and (2) a dynamic learning sample construction strategy, which provides more suitable learning directions for particles with different fitness levels. By synergizing these mechanisms, DPCLSO aims to achieve a more effective exploration–exploitation balance throughout the search process. Extensive experiments on the CEC2010 and CEC2013 benchmark suites demonstrate the superiority of DPCLSO, achieving the highest overall ranking in the Friedman test on both suites. In particular, it obtains superior or competitive results on at least 14 out of 20 functions in the CEC2010 benchmark. These results demonstrate that DPCLSO is a competitive and effective alternative for large-scale optimization problems.

Keywords:

Large-scale optimization

Swarm optimization

Exploration-exploitation balance

Probability-controlled learning

Dynamic learning strategy

A Dynamic Probability-Controlled Learning Swarm Optimizer for large-scale optimization

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