.png){kind=icon}
Return
Potential-driven multi-learning particle swarm optimisation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2025.101993.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The particle swarm optimisation (PSO) algorithm is a simple and effective metaheuristic algorithm. However, its search strategy may lead to issues such as getting trapped in local optima and premature convergence when solving complex multimodal problems. This paper proposes a Potential-Driven Multi-Learning PSO (PDML-PSO) to enhance the global search capability of the PSO algorithm. In this algorithm, particles are classified into three levels based on their performance: elite particles, potential particles, and regular particles. A multi-learning approach is employed to assign different search priorities to each level. Specifically, a non-traditional selection criterion based on the number of consecutive gradient steps related to fitness is used to classify potential particles. This method retains the advantage of gradient descent in accelerating the search while mitigating its drawback of quickly getting trapped in local optima. To validate the performance of PDML-PSO, tests were conducted on 30-dimensional, 50-dimensional, and 100-dimensional problems from the CEC2017 benchmark suite and on 10-dimensional and 20-dimensional problems from the CEC2022 benchmark suite. The results were compared with those of nine other PSO algorithms. The experimental results demonstrate that PDML-PSO exhibits superior global search capability compared to the nine algorithms. Furthermore, ablation experiments confirmed the effectiveness of the improvements made in PDML-PSO. All experimental results highlight PDMLPSO's performance advantage in solving complex multimodal problems.
Keywords:
Particle swarm optimisation
Global optimisation
Multi-learning
Potential archives
Journal
IF:
8.5
Papers:
2.2K
Citations:
1.0W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSave
A novel improved particle swarm optimization algorithm based on individual difference evolution
PREAI
ShareSaveShareSaveComprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
ShareSaveShareSave View more