In IoT,routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance.The eva-luation of optimal routing and related routing parameters over the dep...In IoT,routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance.The eva-luation of optimal routing and related routing parameters over the deployed net-work environment is challenging.This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory(s-LSTM)and Bi-directional Long Short Term Memory(b-LSTM).It is used to hold the routing information and random routing to attain superior performance.The pro-posed model is trained based on the searching and detection mechanisms to com-pute the packet delivery ratio(PDR),end-to-end(E2E)delay,throughput,etc.The anticipated s-LSTM and b-LSTM model intends to ensure Quality of Service(QoS)even in changing network topology.The performance of the proposed b-LSTM and s-LSTM is measured by comparing the significance of the model with various prevailing approaches.Sometimes,the performance is measured with Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)for mea-suring the error rate of the model.The prediction of error rate is made with Learn-ing-based Stochastic Gradient Descent(L-SGD).This gradual gradient descent intends to predict the maximal or minimal error through successive iterations.The simulation is performed in a MATLAB 2020a environment,and the model performance is evaluated with diverse approaches.The anticipated model intends to give superior performance in contrast to prevailing approaches.展开更多
Crowdsourcing is widely used in various fields to collect goods and services from large participants.Evaluating teaching quality by collecting feedback from experts or students after class is not only delayed but also...Crowdsourcing is widely used in various fields to collect goods and services from large participants.Evaluating teaching quality by collecting feedback from experts or students after class is not only delayed but also not accurate.In this paper,we present a crowdsourcing-based framework to evaluate teaching quality in the classroom using a weighted average operator to aggregate information from students’questionnaires described by linguistic 2-tuple terms.Then we define crowd grade based on similarity degree to distinguish contribution from different students and minimize the abnormal students’impact on the evaluation.The crowd grade would be updated at the end of each feedback so it can guarantee the evaluation accurately.Moreover,a simulated case is shown to illustrate how to apply this framework to assess teaching quality in the classroom.Finally,we developed a prototype and carried out some experiments on a series of real questionnaires and two sets of modified data.The results show that teachers can locate the weak points of teaching and furthermore to identify the abnormal students to improve the teaching quality.Meanwhile,our approach provides a strong tolerance for the abnormal student to make the evaluation more accurate.展开更多
Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems.For optimization problems,metaheuristic algorithm is one of the methods to find its optimal so...Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems.For optimization problems,metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions.Most of the existing metaheuristic algorithms are designed for serial systems.Meanwhile,existing algorithms still have a lot of room for improvement in convergence speed,robustness,and performance.To address these issues,this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization(TCCO)inspired by the process of human team cooperation and competition.The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions.In order to evaluate the performance of the proposed algorithm,this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm(GOA),Seagull Optimization Algorithm(SOA),Whale Optimization Algorithm(WOA)and Sparrow Search Algorithm(SSA).Experiment results of 30 test functions commonly used in the optimization field indicate that,compared with these current advanced metaheuristic algorithms,TCCO has strong competitiveness in both solution accuracy and convergence speed.展开更多
The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs.Quantum-behaved Particle Swarm Optimization(QPSO)algorithm is a kind of metaheuristic algorithms appli...The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs.Quantum-behaved Particle Swarm Optimization(QPSO)algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling.This paper analyzed the motion pattern of particles in a square potential well,given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well(BC-QSPSO).In this novel algorithm,the intrinsic cognitive link between particles’experience information and group sharing information was created by using normal Copula function.After that,the control parameters chosen strategy gives through experiments.Finally,the simulation results of the test functions show that the improved algorithms outperform the original QPSO algorithm and due to the error gradient information will not be over utilized in square potential well,the particles are easy to jump out of the local optimum,the BC-QSPSO is more suitable to solve the functions with correlative variables.展开更多
文摘In IoT,routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance.The eva-luation of optimal routing and related routing parameters over the deployed net-work environment is challenging.This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory(s-LSTM)and Bi-directional Long Short Term Memory(b-LSTM).It is used to hold the routing information and random routing to attain superior performance.The pro-posed model is trained based on the searching and detection mechanisms to com-pute the packet delivery ratio(PDR),end-to-end(E2E)delay,throughput,etc.The anticipated s-LSTM and b-LSTM model intends to ensure Quality of Service(QoS)even in changing network topology.The performance of the proposed b-LSTM and s-LSTM is measured by comparing the significance of the model with various prevailing approaches.Sometimes,the performance is measured with Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)for mea-suring the error rate of the model.The prediction of error rate is made with Learn-ing-based Stochastic Gradient Descent(L-SGD).This gradual gradient descent intends to predict the maximal or minimal error through successive iterations.The simulation is performed in a MATLAB 2020a environment,and the model performance is evaluated with diverse approaches.The anticipated model intends to give superior performance in contrast to prevailing approaches.
文摘Crowdsourcing is widely used in various fields to collect goods and services from large participants.Evaluating teaching quality by collecting feedback from experts or students after class is not only delayed but also not accurate.In this paper,we present a crowdsourcing-based framework to evaluate teaching quality in the classroom using a weighted average operator to aggregate information from students’questionnaires described by linguistic 2-tuple terms.Then we define crowd grade based on similarity degree to distinguish contribution from different students and minimize the abnormal students’impact on the evaluation.The crowd grade would be updated at the end of each feedback so it can guarantee the evaluation accurately.Moreover,a simulated case is shown to illustrate how to apply this framework to assess teaching quality in the classroom.Finally,we developed a prototype and carried out some experiments on a series of real questionnaires and two sets of modified data.The results show that teachers can locate the weak points of teaching and furthermore to identify the abnormal students to improve the teaching quality.Meanwhile,our approach provides a strong tolerance for the abnormal student to make the evaluation more accurate.
基金This research was partially supported by the National Key Research and Development Program of China(2018YFC1507005)Sichuan Science and Technology Program(2020YFG0189,22ZDYF3494)+1 种基金China Postdoctoral Science Foundation(2018M643448)Fundamental Research Funds for the Central Universities,Southwest Minzu University(2022101).
文摘Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems.For optimization problems,metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions.Most of the existing metaheuristic algorithms are designed for serial systems.Meanwhile,existing algorithms still have a lot of room for improvement in convergence speed,robustness,and performance.To address these issues,this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization(TCCO)inspired by the process of human team cooperation and competition.The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions.In order to evaluate the performance of the proposed algorithm,this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm(GOA),Seagull Optimization Algorithm(SOA),Whale Optimization Algorithm(WOA)and Sparrow Search Algorithm(SSA).Experiment results of 30 test functions commonly used in the optimization field indicate that,compared with these current advanced metaheuristic algorithms,TCCO has strong competitiveness in both solution accuracy and convergence speed.
基金This research was funded by National Key Research and Development Program of China(Grant No.2018YFC1507005)China Postdoctoral Science Foundation(Grant No.2018M643448)+1 种基金Sichuan Science and Technology Program(Grant No.2019YFG0110)Fundamental Research Funds for the Central Universities,Southwest Minzu University(Grant No.2019NQN22).
文摘The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs.Quantum-behaved Particle Swarm Optimization(QPSO)algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling.This paper analyzed the motion pattern of particles in a square potential well,given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well(BC-QSPSO).In this novel algorithm,the intrinsic cognitive link between particles’experience information and group sharing information was created by using normal Copula function.After that,the control parameters chosen strategy gives through experiments.Finally,the simulation results of the test functions show that the improved algorithms outperform the original QPSO algorithm and due to the error gradient information will not be over utilized in square potential well,the particles are easy to jump out of the local optimum,the BC-QSPSO is more suitable to solve the functions with correlative variables.
