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Development of an Improved GWO Algorithm for Solving Optimal Paths in Complex Vertical Farms with Multi-Robot Multi-Tasking

Author

Listed:
  • Jiazheng Shen

    (Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia)

  • Tang Sai Hong

    (Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia)

  • Luxin Fan

    (Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia)

  • Ruixin Zhao

    (Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia)

  • Mohd Khairol Anuar b. Mohd Ariffin

    (Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia)

  • Azizan bin As’arry

    (Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia)

Abstract

As the global population grows, achieving Zero Hunger by 2030 presents a significant challenge. Vertical farming technology offers a potential solution, making the path planning of agricultural robots in vertical farms a research priority. This study introduces the Vertical Farming System Multi-Robot Trajectory Planning (VFSMRTP) model. To optimize this model, we propose the Elitist Preservation Differential Evolution Grey Wolf Optimizer (EPDE-GWO), an enhanced version of the Grey Wolf Optimizer (GWO) incorporating elite preservation and differential evolution. The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). The experimental results demonstrate that EPDE-GWO reduces path length by 24.6%, prevents premature convergence, and exhibits strong global search capabilities. Thanks to the DE and EP strategies, the EPDE-GWO requires fewer iterations to reach the optimal solution, offers strong stability and robustness, and consistently finds the optimal solution at a high frequency. These attributes are particularly significant in the context of vertical farming, where optimizing robotic path planning is essential for maximizing operational efficiency, reducing energy consumption, and improving the scalability of farming operations.

Suggested Citation

  • Jiazheng Shen & Tang Sai Hong & Luxin Fan & Ruixin Zhao & Mohd Khairol Anuar b. Mohd Ariffin & Azizan bin As’arry, 2024. "Development of an Improved GWO Algorithm for Solving Optimal Paths in Complex Vertical Farms with Multi-Robot Multi-Tasking," Agriculture, MDPI, vol. 14(8), pages 1-26, August.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1372-:d:1457290
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    References listed on IDEAS

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    1. Luis Emmi & Roemi Fernández & Pablo Gonzalez-de-Santos & Matteo Francia & Matteo Golfarelli & Giuliano Vitali & Hendrik Sandmann & Michael Hustedt & Merve Wollweber, 2023. "Exploiting the Internet Resources for Autonomous Robots in Agriculture," Agriculture, MDPI, vol. 13(5), pages 1-22, May.
    2. Jeffrey K. O'Hara & Jeffrey Lin, 2020. "Population Density and Local Food Market Channels," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(3), pages 477-496, September.
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