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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An efficient assignment of multiple agricultural machinery tasks based on Chaotic Cauchy Elite Variable Snake

Ruoxue Xiang1, Xiang Liu1, Min Tian1

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.

Plos One
|December 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm, Chaotic Cauchy Elite Variation Snake Optimisation Algorithm (CCEVSOA), for efficient task allocation in smart farms. CCEVSOA significantly reduces machinery operation time and improves coordination, boosting productivity and minimizing resource waste.

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Area of Science:

  • Agricultural Engineering
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Unmanned smart farms face challenges in multi-machine task allocation, leading to inefficiencies.
  • Current strategies result in suboptimal machinery deployment, reduced productivity, and wasted resources.

Purpose of the Study:

  • To develop a novel task allocation model and optimization algorithm for agricultural machinery.
  • To enhance the efficiency and economic viability of task allocation in smart farming.

Main Methods:

  • Introduction of a novel task allocation model considering machine speed, turning time, and fuel consumption.
  • Development and application of the Chaotic Cauchy Elite Variation Snake Optimisation Algorithm (CCEVSOA).
  • CCEVSOA utilizes chaotic and Cauchy operators with elite evolution for improved search and convergence.

Main Results:

  • CCEVSOA demonstrated superior performance and a faster convergence rate compared to existing algorithms (SO, GA, CSA, WOA, IBES).
  • Significant reductions in collaborative task allocation time were achieved: 103 min (vs. SO), 89 min (vs. GA), 106 min (vs. CSA), 97 min (vs. WOA), and 36 min (vs. IBES).
  • Efficiency improvements ranged from 5.5% to 14.6%.

Conclusions:

  • CCEVSOA provides a more rational and economically efficient approach to multi-machine task allocation in smart farms.
  • The optimized allocation schemes enhance agricultural machinery productivity while minimizing resource wastage.
  • This research contributes to advancing intelligent agricultural systems through improved operational efficiency.