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NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment

To improve the performance of intelligent products and reasonably distribute the load of the loading robot, a multi-objective, and multi-objective (Traveling-salesman problem, TSP) mathematical model was established. A genetic algorithm based on speed invariant and the elite algorithm is proposed to...

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Main Authors: Shen, jiazheng, Tang, Sai Hong, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan, Wang, Xinming
Format: Article
Language:English
Published: Elsevier 2024
Online Access:http://psasir.upm.edu.my/id/eprint/113307/1/113307.pdf
http://psasir.upm.edu.my/id/eprint/113307/
https://www.sciencedirect.com/science/article/pii/S1877750324001662
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spelling my.upm.eprints.1133072024-11-20T05:58:10Z http://psasir.upm.edu.my/id/eprint/113307/ NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment Shen, jiazheng Tang, Sai Hong Mohd Ariffin, Mohd Khairol Anuar As’arry, Azizan Wang, Xinming To improve the performance of intelligent products and reasonably distribute the load of the loading robot, a multi-objective, and multi-objective (Traveling-salesman problem, TSP) mathematical model was established. A genetic algorithm based on speed invariant and the elite algorithm is proposed to solve the multi-TSP assignment problem. To ensure the integration of the population, a population resettlement strategy with elite lakes was proposed to improve the probability of population transfer to the best Pareto solution. The experiment verified that this strategy can approach the optimal solution more closely during the population convergence process, and compared it with traditional Multi TSP algorithms and single function multi-objective Multi TSP algorithms. By comparing the total distance and maximum deviation of multiple robot systems, it is proven that this algorithm can effectively balance the path length of each robot in task allocation. From the research results, it can be seen that in genetic algorithms, resetting the population after reaching precocity can maintain the optimization characteristics of the population and have a high probability of obtaining Pareto solutions. At the same time, storing elite individuals from previous convergent populations for optimization can better obtain Pareto solutions. Elsevier 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/113307/1/113307.pdf Shen, jiazheng and Tang, Sai Hong and Mohd Ariffin, Mohd Khairol Anuar and As’arry, Azizan and Wang, Xinming (2024) NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment. Journal of Computational Science, 81. art. no. 102373. pp. 1-8. ISSN 1877-7503 https://www.sciencedirect.com/science/article/pii/S1877750324001662 10.1016/j.jocs.2024.102373
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 To improve the performance of intelligent products and reasonably distribute the load of the loading robot, a multi-objective, and multi-objective (Traveling-salesman problem, TSP) mathematical model was established. A genetic algorithm based on speed invariant and the elite algorithm is proposed to solve the multi-TSP assignment problem. To ensure the integration of the population, a population resettlement strategy with elite lakes was proposed to improve the probability of population transfer to the best Pareto solution. The experiment verified that this strategy can approach the optimal solution more closely during the population convergence process, and compared it with traditional Multi TSP algorithms and single function multi-objective Multi TSP algorithms. By comparing the total distance and maximum deviation of multiple robot systems, it is proven that this algorithm can effectively balance the path length of each robot in task allocation. From the research results, it can be seen that in genetic algorithms, resetting the population after reaching precocity can maintain the optimization characteristics of the population and have a high probability of obtaining Pareto solutions. At the same time, storing elite individuals from previous convergent populations for optimization can better obtain Pareto solutions.
format Article
author Shen, jiazheng
Tang, Sai Hong
Mohd Ariffin, Mohd Khairol Anuar
As’arry, Azizan
Wang, Xinming
spellingShingle Shen, jiazheng
Tang, Sai Hong
Mohd Ariffin, Mohd Khairol Anuar
As’arry, Azizan
Wang, Xinming
NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment
author_facet Shen, jiazheng
Tang, Sai Hong
Mohd Ariffin, Mohd Khairol Anuar
As’arry, Azizan
Wang, Xinming
author_sort Shen, jiazheng
title NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment
title_short NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment
title_full NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment
title_fullStr NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment
title_full_unstemmed NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment
title_sort nsga-iii algorithm for optimizing robot collaborative task allocation in the internet of things environment
publisher Elsevier
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/113307/1/113307.pdf
http://psasir.upm.edu.my/id/eprint/113307/
https://www.sciencedirect.com/science/article/pii/S1877750324001662
_version_ 1817844623338373120
score 13.235362

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