An efficient assignment of multiple agricultural machinery tasks based on Chaotic Cauchy Elite Variable Snake Optimization Algorithm

Ruoxue Xiang, Xiang Liu, Min Tian, Chenglong Ban, Rongxuan Li and Jie Zhou

PLOS ONE, 2025, vol. 20, issue 12, 1-28

Abstract: Task allocation for agricultural machinery constitutes a critical challenge in multi-machine coordination within unmanned smart farms. Enhancing the efficiency of task allocation remains an urgent research problem. Current machinery often suffers from inefficient allocation strategies and unprocessed field areas, which lead to reduced productivity and unnecessary resource consumption. This study develops a novel task allocation model incorporating machine speed, turning time, and fuel consumption to overcome these limitations. In addition, a Chaotic Cauchy Elite Variation Snake Optimisation Algorithm (CCEVSOA) is introduced. Specifically, the algorithm employs a chaotic operator tailored for multi-machine coordination scenarios, ensuring a more uniform distribution of initial solutions across the search space. Moreover, integrating an enhanced Cauchy operator with an elite evolution strategy enlarges the search domain and mitigates premature convergence, reducing overall operation time and improving coordination efficiency. Extensive experiments verify that CCEVSOA achieves superior performance with a markedly faster convergence rate. When compared with the Snake Optimization Algorithm (SO), Genetic Algorithm (GA), Clone Selection Algorithm (CSA), Whale Optimization Algorithm (WOA), and the Improved Buzzard Evolution Algorithm based on Lévy Flight and Simulated Annealing (IBES), CCEVSOA reduces collaborative task allocation time by 103, 89, 106, 97, and 36 minutes, corresponding to efficiency improvements of 14.25%, 12.55%, 14.6%, 13.53%, and 5.5%, respectively. These findings demonstrate that optimising multi-machine task allocation through CCEVSOA yields more rational and economically efficient distribution schemes for agricultural machinery, effectively enhancing productivity while minimising resource wastage.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0337219 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 37219&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0337219

DOI: 10.1371/journal.pone.0337219

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().