December 7, 2025SymmetryOpen Access

A High-Performance Learning Particle Swarm Optimization Based on the Knowledge of Individuals for Large-Scale Problems

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This approach demonstrates effective optimization in large-scale problems, offering enhanced performance and algorithm stability.

HPLPSO balances global exploration and local exploitation, improving convergence in large-scale problems.
The algorithm achieves theoretical optimal values across multiple dimensions, showing reliable performance.
Evaluation involved classical test functions across 100 to 1000 dimensions, highlighting its broad applicability.
This optimization method may enhance solutions to complex challenges in diverse fields.

Xu et al. (2025) studied this question.

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