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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine 被引量:9
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作者 孙泽文 顾幸生 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第8期1779-1788,共10页
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor... The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms. 展开更多
关键词 hybrid estimation of distribution algorithm teaching learning based optimization strategy hybrid flow shop unrelated parallel machine scheduling
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Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem
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作者 Nianbo Kang Zhonghua Miao +2 位作者 Quan-Ke Pan Weimin Li M.Fatih Tasgetiren 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第5期1249-1265,共17页
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 i... 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 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 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. 展开更多
关键词 genetic algorithm heuristic algorithm Multi-Weeding Robot Task Assignment(MWRTA) teaching optimization algorithm
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