In order to achieve dynamical optimization of mobility load balancing,we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameter...In order to achieve dynamical optimization of mobility load balancing,we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameters.To this end,a method of Handover Parameters Adjustment for Conflict Avoidance(HPACA)is proposed.Considering the movement of users,HPCAC can dynamically adjust handover range to optimize the mobility load balancing.The movement of users is an important factor of handover,which has a dramatic impact on system performance.The numerical evaluation results show the proposed approach outperforms the existing method in terms of throughput,call blocking ratio,load balancing index,radio link failure ratio,ping-pong handover ratio and call dropping ratio.展开更多
A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rat...A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61071118the National Basic Research Program of China(973 Program)under Grant No.2012CB316004+1 种基金Special Fund of Chongqing Key Laboratory(CSTC)Chongqing Municipal Education Commission’s Science and Technology Research Project under Grant No.KJ111506
文摘In order to achieve dynamical optimization of mobility load balancing,we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameters.To this end,a method of Handover Parameters Adjustment for Conflict Avoidance(HPACA)is proposed.Considering the movement of users,HPCAC can dynamically adjust handover range to optimize the mobility load balancing.The movement of users is an important factor of handover,which has a dramatic impact on system performance.The numerical evaluation results show the proposed approach outperforms the existing method in terms of throughput,call blocking ratio,load balancing index,radio link failure ratio,ping-pong handover ratio and call dropping ratio.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of China
文摘A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
