Multi-Task Particle Swarm Optimization With Dynamic Neighbor and Level-Based Inter-Task Learning

Existing multifactorial particle swarm optimization algorithms treat all particles equally with a consistent inter-task exemplar selection and generation strategy. This may lead to poor performance when the algorithm searches partial optimal areas belonging to different tasks at the later stage. In pedagogy, teachers teach students in different levels distinctively under their cognitive and learning abilities. Inspired by this idea, in this work, we devise a novel level-based inter-task learning strategy upon a dynamic local topology of inter-task particles. The proposed method separates particles into several levels and assigns particles to different levels with distinct inter-task learning methods. Specifically, we propose a level-based inter-task learning strategy to transfer sharing information among the cross-task neighborhood. By assigning the particles with diverse search preferences, the algorithm is able to explore the search space by using the cross-task knowledge, while reserving an ability to refine the search area. In addition, to address the issue of inter-task neighbor selection, we reform dynamically the local topology structure across the inter-task particles by methodical sampling, evaluating and selecting processes. Experimental results on the benchmark problems demonstrate that the proposed method enables the efficient cross-domain information transfer via the level-based inter-task learning.

Automated summary

This study introduces a level-based inter-task learning strategy that categorizes particles into different levels and employs various inter-task learning methods. This strategy enhances the transfer of shared information among cross-task neighborhoods, allowing the algorithm to efficiently explore the search space and refine search areas.