A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimi...A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimization with differential evo- lution （HGWO）. Because basic grey wolf optimization （GWO） is easy to fall into stagnation when it carries out the operation of at- tacking prey, and differential evolution （DE） is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE＇s strong searching ability. The proposed algorithm can accele- rate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.展开更多
Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to f...Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.展开更多
战场频率指配能够在复杂电磁环境下将战场中有限的频谱资源指配至用频装备,对用频装备作战的效能发挥与电磁频谱作战筹划具有重要意义。本文从数学模型、求解算法两个方面分别总结归纳了静态频率指配问题(static frequency assignment p...战场频率指配能够在复杂电磁环境下将战场中有限的频谱资源指配至用频装备,对用频装备作战的效能发挥与电磁频谱作战筹划具有重要意义。本文从数学模型、求解算法两个方面分别总结归纳了静态频率指配问题(static frequency assignment problem,S-FAP)与动态频率指配问题(dynamic frequency assignment problem,D-FAP)的研究现状,分析评述了模型的适用性及算法优缺点,最后对战场频率指配未来的发展趋势进行了展望。展开更多
如何提供不同的服务质量(quality of service,简称QoS)是互联网络面临的一个重要问题,而服务质量路由(quality-of-service routing,简称QoSR)则是其中的核心技术和热点问题.QoSR的主要作用是为QoS业务请求寻找可行路径,这体现了QoSR的...如何提供不同的服务质量(quality of service,简称QoS)是互联网络面临的一个重要问题,而服务质量路由(quality-of-service routing,简称QoSR)则是其中的核心技术和热点问题.QoSR的主要作用是为QoS业务请求寻找可行路径,这体现了QoSR的两个目标:(1) 满足业务QoS需求;(2) 最大限度地提高网络利用率.由于QoSR是NP完全问题,研究者们设计了很多启发式算法进行了广泛深入的研究.在有权图和QoS度量的基础上介绍了QoSR的基本概念,详细分析了面向单播应用的QoSR算法中的热点问题,并按照所求解的问题类型和求解方法,将这些算法分成以下几类:多项式非启发类、伪多项式非启发类、探测类、限定QoS度量类、路径子空间搜索类、QoS度量相关类、花费函数类和概率求解类.在分析每类中典型算法的基础上,总结和对比了各类的特点,进而详细剖析了算法的有效性,并基于此总结了基于概率模型求解QoSR问题的方法.最后指出了该领域中需要进一步研究的热点问题.展开更多
基金supported by the National Natural Science Foundation of China(6076600161105004)+1 种基金the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(YQ14110)the Program for Innovative Research Team of Guilin University of Electronic Technology(IRTGUET)
文摘A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimization with differential evo- lution （HGWO）. Because basic grey wolf optimization （GWO） is easy to fall into stagnation when it carries out the operation of at- tacking prey, and differential evolution （DE） is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE＇s strong searching ability. The proposed algorithm can accele- rate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.
基金supported by the Military Science Project of the National Social Science Foundation of China(15GJ003-141)
文摘Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.
文摘战场频率指配能够在复杂电磁环境下将战场中有限的频谱资源指配至用频装备,对用频装备作战的效能发挥与电磁频谱作战筹划具有重要意义。本文从数学模型、求解算法两个方面分别总结归纳了静态频率指配问题(static frequency assignment problem,S-FAP)与动态频率指配问题(dynamic frequency assignment problem,D-FAP)的研究现状,分析评述了模型的适用性及算法优缺点,最后对战场频率指配未来的发展趋势进行了展望。
