Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In ...Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.展开更多
As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent task...As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.展开更多
The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contribute...The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.展开更多
Purpose–The purpose of this paper is to propose a novel improved teaching and learning-based algorithm(TLBO)to enhance its convergence ability and solution accuracy,making it more suitable for solving large-scale opt...Purpose–The purpose of this paper is to propose a novel improved teaching and learning-based algorithm(TLBO)to enhance its convergence ability and solution accuracy,making it more suitable for solving large-scale optimization issues.Design/methodology/approach–Utilizing multiple cooperation mechanisms in teaching and learning processes,an improved TBLO named CTLBO(collectivism teaching-learning-based optimization)is developed.This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes.Applying modularizationidea,based on the configuration structure of operators ofCTLBO,six variants ofCTLBOare constructed.Foridentifying the best configuration,30 general benchmark functions are tested.Then,three experiments using CEC2020(2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms.At last,a large-scale industrial engineering problem is taken as the application case.Findings–Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO.Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems.The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem,while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c,revealing that CTLBO and its variants can far outperform other algorithms.CTLBO is an excellent algorithm for solving large-scale complex optimization issues.Originality/value–The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism,self-learning mechanism in teaching and group teaching mechanism.CTLBO has important application value in solving large-scale optimization problems.展开更多
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
The energy consumption of a teaching building can be effectively reduced by timetable optimization.However,in most studies that explore methods to reduce building energy consumption by course timetable optimization,se...The energy consumption of a teaching building can be effectively reduced by timetable optimization.However,in most studies that explore methods to reduce building energy consumption by course timetable optimization,self-study activities are not considered.In this study,an MATLAB-EnergyPlus joint simulation model was constructed based on the Building Controls Virtual Test Bed platform to reduce building energy consumption by optimizing the course schedule and opening strategy of self-study rooms in a holistic way.The following results were obtained by taking a university in Xi’an as an example:(1)The energy saving percentages obtained by timetabling optimization during the heating season examination week,heating season non-examination week,cooling season examination week,and cooling season non-examination week are 35%,29.4%,13.4%,and 13.4%,respectively.(2)Regarding the temporal arrangement,most courses are scheduled in the morning during the cooling season and afternoon during the heating season.Regarding the spatial arrangement,most courses are arranged in the central section of the middle floors of the building.(3)During the heating season,the additional building energy consumption incurred by the opening of self-study rooms decreases when duty heating temperature increases.展开更多
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
In northern China,university classrooms are often densely populated,and students have limited means of thermal adaptation during lectures.Considering the significant differences in the thermal environment of the class...In northern China,university classrooms are often densely populated,and students have limited means of thermal adaptation during lectures.Considering the significant differences in the thermal environment of the classroom throughout different periods,changing the patterns of classroom utilization is a feasible way to improve students’thermal comfort during classes and ensure learning efficiency.A university teaching building in Xi’an is considered an example in this study.The indoor and outdoor thermal environment parameters of the teaching building were measured in the autumn semester,and the students’thermal sensation was investigated.On this basis,a model for optimizing university timetables was developed to minimize students’thermal discomfort in classrooms.The study results showed:1)During non-heating seasons,students felt comfortable in all periods,except for the third class period(14:00-15:30),during which they felt slightly hot.During the heating season,students felt slightly cold in the first class period(8:30-10:00),slightly hot in the third class period,and comfortable in the second(10:30-12:00)and fourth(16:00-17:30)class periods.2)Compared to the general schedule,the optimized timetable decreased first period classes by 14 and increased fourth period classes by 13,with minimal changes elsewhere.Adopting this approach,students’thermal discomfort time during classes in the autumn semester was shortened by 6.16%.3)The students’thermal discomfort time reduction rate obtained by timetabling optimization during the non-heating season,heating season are 0.78%,8.91%,respectively.The effect of reducing students’thermal discomfort is more pronounced during the heating season.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51275366,50875190,51305311)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20134219110002)
文摘Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
文摘As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.
文摘The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.
基金This research is funded by the National Natural Science Foundation of China(#71772191).
文摘Purpose–The purpose of this paper is to propose a novel improved teaching and learning-based algorithm(TLBO)to enhance its convergence ability and solution accuracy,making it more suitable for solving large-scale optimization issues.Design/methodology/approach–Utilizing multiple cooperation mechanisms in teaching and learning processes,an improved TBLO named CTLBO(collectivism teaching-learning-based optimization)is developed.This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes.Applying modularizationidea,based on the configuration structure of operators ofCTLBO,six variants ofCTLBOare constructed.Foridentifying the best configuration,30 general benchmark functions are tested.Then,three experiments using CEC2020(2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms.At last,a large-scale industrial engineering problem is taken as the application case.Findings–Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO.Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems.The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem,while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c,revealing that CTLBO and its variants can far outperform other algorithms.CTLBO is an excellent algorithm for solving large-scale complex optimization issues.Originality/value–The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism,self-learning mechanism in teaching and group teaching mechanism.CTLBO has important application value in solving large-scale optimization problems.
基金supported by the National Natural Science Foundation of China(Nos.62273221 and 61973203)the Program of Shanghai Academic/Technology Research Leader(No.21XD1401000)the Shanghai Key Laboratory of Power Station Automation Technology.
文摘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.
基金supported by the National Natural Science Foundation of China (52008328)National Key Research and Development Project (2018YFD1100202)+1 种基金the Science and Technology Department of Shaanxi Province (2020SF-393,2018ZDCXL-SF-03-04)the State Key Laboratory of Green Building in Western China (LSZZ202009).
文摘The energy consumption of a teaching building can be effectively reduced by timetable optimization.However,in most studies that explore methods to reduce building energy consumption by course timetable optimization,self-study activities are not considered.In this study,an MATLAB-EnergyPlus joint simulation model was constructed based on the Building Controls Virtual Test Bed platform to reduce building energy consumption by optimizing the course schedule and opening strategy of self-study rooms in a holistic way.The following results were obtained by taking a university in Xi’an as an example:(1)The energy saving percentages obtained by timetabling optimization during the heating season examination week,heating season non-examination week,cooling season examination week,and cooling season non-examination week are 35%,29.4%,13.4%,and 13.4%,respectively.(2)Regarding the temporal arrangement,most courses are scheduled in the morning during the cooling season and afternoon during the heating season.Regarding the spatial arrangement,most courses are arranged in the central section of the middle floors of the building.(3)During the heating season,the additional building energy consumption incurred by the opening of self-study rooms decreases when duty heating temperature increases.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘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.
基金supported by National Natural Science Foundation of China(52318109)University-Industry Collaborative Education Program(220600551163453)+1 种基金Science and Technology Department of Shaanxi Province(2020SF-393)Shaanxi Province Innovation Capability Support Program-Youth Science and Technology New Star Project(2023KJXX-043).
文摘In northern China,university classrooms are often densely populated,and students have limited means of thermal adaptation during lectures.Considering the significant differences in the thermal environment of the classroom throughout different periods,changing the patterns of classroom utilization is a feasible way to improve students’thermal comfort during classes and ensure learning efficiency.A university teaching building in Xi’an is considered an example in this study.The indoor and outdoor thermal environment parameters of the teaching building were measured in the autumn semester,and the students’thermal sensation was investigated.On this basis,a model for optimizing university timetables was developed to minimize students’thermal discomfort in classrooms.The study results showed:1)During non-heating seasons,students felt comfortable in all periods,except for the third class period(14:00-15:30),during which they felt slightly hot.During the heating season,students felt slightly cold in the first class period(8:30-10:00),slightly hot in the third class period,and comfortable in the second(10:30-12:00)and fourth(16:00-17:30)class periods.2)Compared to the general schedule,the optimized timetable decreased first period classes by 14 and increased fourth period classes by 13,with minimal changes elsewhere.Adopting this approach,students’thermal discomfort time during classes in the autumn semester was shortened by 6.16%.3)The students’thermal discomfort time reduction rate obtained by timetabling optimization during the non-heating season,heating season are 0.78%,8.91%,respectively.The effect of reducing students’thermal discomfort is more pronounced during the heating season.
