Multi-objective particle swarm optimization algorithm for task allocation and archived guided mutation strategies

Multi-objective particle swarm optimization algorithm for task allocation and archived guided mutation strategies

Abstract In this paper, we propose a novel multi-objective particle swarm optimization algorithm with a task allocation and archive-guided mutation strategy (TAMOPSO), which effectively solves the problem of inefficient search in traditional algorithms by assigning different evolutionary tasks to pa...

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Bibliographic Details
Main Authors: Jianjie Chen, Yanmin Liu, Yi Luo, Aijia Ouyang, Jie Yang, Wuer Bai
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Multi-objective particle swarm optimization
Subpopulation partitioning
Task allocation
Lévy flight strategy
Individual optimal selection
Online Access:https://doi.org/10.1038/s41598-025-99730-1
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https://doi.org/10.1038/s41598-025-99730-1

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