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

Tsinghua Science and Technology 2024年第5期29卷 1249-1265页

作者： Nianbo Kang Zhonghua Miao Quan-Ke Pan Weimin Li M.Fatih Tasgetiren School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Industrial Engineering Department Baskent UniversityAnkara 06790Türkiye

With the emergence of the artificial intelligence era,all kinds of robots are traditionally used in agricultural ***,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 ***,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 *** the MOTLBO algorithm,a heuristicbased initialization comprising an improved Nawaz Enscore,and Ham(NEH)heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and *** 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 ***,a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the *** results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

关键词： genetic algorithm heuristic algorithm Multi-Weeding Robot Task Assignment(MWRTA) teaching optimization algorithm

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