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Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking

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-Robo...

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Main Authors: Shen, Jiazheng, Hong, Tang Sai, Fan, Luxin, Zhao, Ruixin, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute 2024
Online Access:http://psasir.upm.edu.my/id/eprint/114733/1/114733.pdf
http://psasir.upm.edu.my/id/eprint/114733/
https://www.mdpi.com/2077-0472/14/8/1372
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spelling my.upm.eprints.1147332025-02-03T03:19:20Z http://psasir.upm.edu.my/id/eprint/114733/ Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking Shen, Jiazheng Hong, Tang Sai Fan, Luxin Zhao, Ruixin Mohd Ariffin, Mohd Khairol Anuar As’arry, Azizan 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. Multidisciplinary Digital Publishing Institute 2024-08-15 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114733/1/114733.pdf Shen, Jiazheng and Hong, Tang Sai and Fan, Luxin and Zhao, Ruixin and Mohd Ariffin, Mohd Khairol Anuar and As’arry, Azizan (2024) Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking. Agriculture, 14 (8). art. no. 1372. pp. 1-26. ISSN 2077-0472 https://www.mdpi.com/2077-0472/14/8/1372 10.3390/agriculture14081372
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Article
author Shen, Jiazheng
Hong, Tang Sai
Fan, Luxin
Zhao, Ruixin
Mohd Ariffin, Mohd Khairol Anuar
As’arry, Azizan
spellingShingle Shen, Jiazheng
Hong, Tang Sai
Fan, Luxin
Zhao, Ruixin
Mohd Ariffin, Mohd Khairol Anuar
As’arry, Azizan
Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
author_facet Shen, Jiazheng
Hong, Tang Sai
Fan, Luxin
Zhao, Ruixin
Mohd Ariffin, Mohd Khairol Anuar
As’arry, Azizan
author_sort Shen, Jiazheng
title Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
title_short Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
title_full Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
title_fullStr Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
title_full_unstemmed Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
title_sort development of an improved gwo algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
publisher Multidisciplinary Digital Publishing Institute
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/114733/1/114733.pdf
http://psasir.upm.edu.my/id/eprint/114733/
https://www.mdpi.com/2077-0472/14/8/1372
_version_ 1823093258189799424
score 13.235362

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