With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Ex...With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.展开更多
This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly...This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.展开更多
基金This work was supported by the National Key Research and Development Program of China(No.2018YFC1604000)the National Natural Science Foundation of China(Nos.61806138,61772478,U1636220,61961160707,and 61976212)+2 种基金the Key R&D Program of Shanxi Province(High Technology)(No.201903D121119)the Key R&D Program of Shanxi Province(International Cooperation)(No.201903D421048)the Key R&D Program(International Science and Technology Cooperation Project)of Shanxi Province,China(No.201903D421003).
文摘With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.
基金supported by National Natural Science Foundation of China under Grant No.60971083National International Science and Technology Cooperation Project of China (No.2010DFA11320)
文摘This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.