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Tsinghua Science and Technology 2024, 29(5): 1249-1265 https://doi.org/10.26599/TST.2023.9010075
Open Access | Issue | Published: 02 May 2024

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Show Author's Information Nianbo Kang1,  Zhonghua Miao1,  Quan-Ke Pan1( ),  Weimin Li2,  M. Fatih Tasgetiren3
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Industrial Engineering Department, Baskent University, Ankara 06790, Türkiye
Keywords:
genetic algorithm, heuristic algorithm, Multi-Weeding Robot Task Assignment (MWRTA), teaching optimization algorithm
Cite this article:
Kang N, Miao Z, Pan Q-K, et al. Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem. Tsinghua Science and Technology, 2024, 29(5): 1249-1265. https://doi.org/10.26599/TST.2023.9010075
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Abstract Full text About this article

With the emergence of the artificial intelligence era, all kinds of robots are traditionally used in agricultural production. However, studies concerning the robot task assignment problem in the agriculture field, which is closely related to the cost and efficiency of a smart farm, are limited. Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addressed in this paper to minimize the maximum completion time and residual herbicide. A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristic-based initialization comprising an improved Nawaz Enscore, and Ham (NEH) heuristic and maximum load-based heuristic is used to generate an initial population with a high level of quality and diversity. An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature. Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.


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About this article

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Show Author's information Nianbo Kang1, Zhonghua Miao1, Quan-Ke Pan1( ), Weimin Li2, M. Fatih Tasgetiren3
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Industrial Engineering Department, Baskent University, Ankara 06790, Türkiye

Abstract

With the emergence of the artificial intelligence era, all kinds of robots are traditionally used in agricultural production. However, studies concerning the robot task assignment problem in the agriculture field, which is closely related to the cost and efficiency of a smart farm, are limited. Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addressed in this paper to minimize the maximum completion time and residual herbicide. A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristic-based initialization comprising an improved Nawaz Enscore, and Ham (NEH) heuristic and maximum load-based heuristic is used to generate an initial population with a high level of quality and diversity. An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature. Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

Keywords: genetic algorithm, heuristic algorithm, Multi-Weeding Robot Task Assignment (MWRTA), teaching optimization algorithm

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Publication history

Received: 07 May 2023
Revised: 15 July 2023
Accepted: 25 July 2023
Published: 02 May 2024
Issue date: October 2024

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© The Author(s) 2024.

Acknowledgements

Acknowledgment

This research was partially supported by the National Natural Science Foundation of China (Nos. 62273221 and 61973203), the Program of Shanghai Academic/ Technology Research Leader (No. 21XD1401000), and the Shanghai Key Laboratory of Power Station Automation Technology.

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