.png){kind=icon}
.png){kind=icon}
Return
Multitask particle swarm optimization algorithm leveraging variable chunking and local meta-knowledge transfer
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2024.101823.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The Particle Swarm Optimization (PSO) algorithm is widely applied in multitask optimization because of its simplicity and rapid convergence. Nevertheless, the original Multitask PSO (MTPSO) algorithm rarely utilizes local similarity for dissimilar or less similar tasks and lacks mechanisms for information exchange (IE) among variables of different dimensions. This study presents a novel MTPSO based on variable chunking and local metaknowledge transfer (MKT) to leverage the local information of individuals and enable IE among variables of varying dimensions. First, a construction-assisted transfer individual strategy is proposed. Using variable chunking and Latin hypercube sampling, an auxiliary transfer individual is constructed for each task. Using this individual to guide population evolution can promote IE among individuals with different dimensions and effectively enhance individual diversity. Subsequently, the populations are clustered to assess the local similarities between tasks. Based on these similarities, the MKT strategy is designed to promote mutual learning opportunities among locally similar populations. On the adaptive side, an adaptive matching probability strategy is proposed to help the algorithm dynamically adjust the transfer probability according to the task similarities, effectively reducing the occurrence of negative transfers. Finally, the algorithm is evaluated on the CEC 2017 problem set and two real-world multitask optimization problems, and its performance is compared with 12 other typical multitask optimization algorithms. The results show that the proposed algorithm outperforms most of the compared algorithms both in terms of convergence speed and accuracy. Meanwhile, variant experiments demonstrate the effectiveness of the proposed strategies.
Keywords:
Particle swarm optimization
Multitask
Local similarity
Meta-knowledge transfer
Adaptive matching probability
Journal
IF:
8.5
Papers:
2.1K
Citations:
1.0W