For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best coo...For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best cooperative mechanism(BCM)is proposed for CR with EH. To get the optimal estimation performance, a quantum fireworks algorithm(QFA) is designed to resolve the difficulties of maximal throughput and EH, and the proposed cooperative mechanism is called as QFA-BCM. The proposed QFA combines the advantages of quantum computation theory with the fireworks algorithm(FA). Thus the QFA is able to obtain the optimal solution and its convergence performance is proved. By using the new cooperation mechanism and computing algorithm, the proposed QFA-BCM method can achieve comparable maximal throughput in the new timeslot structure. Simulation results have proved that the QFA-BCM method is superior to previous non-cooperative and cooperative mechanisms.展开更多
为了满足供应链物流的不同需求,考虑多种车型、车辆容量、车辆油耗、车辆最大配送距离等约束条件,以最小油耗、最短配送距离为目标,建立多车型供应链物流运输调度模型(Multi-Type Vehicle Routing Problem in Supply Chain,MTVRPSC),并...为了满足供应链物流的不同需求,考虑多种车型、车辆容量、车辆油耗、车辆最大配送距离等约束条件,以最小油耗、最短配送距离为目标,建立多车型供应链物流运输调度模型(Multi-Type Vehicle Routing Problem in Supply Chain,MTVRPSC),并提出一种混沌烟花算法求解该模型。该算法以烟花算法为核心,提出一种编解码策略实现连续空间到MTVRPSC离散空间的映射,重新定义算法的适应度函数、适应度值和适应度的比较方法,并采用混沌初始化策略和混沌搜索策略来增强算法收敛效果。实验结果表明,所提出的算法在求解MTVRPSC时具有较强的寻优能力和稳定性。展开更多
为了更好地满足云计算中用户的服务质量(quality of service,QoS)需求,合理利用云数据中心的资源,以任务的执行时间和虚拟机的负载均衡作为优化的目标对象,提出了一种基于烟花算法(fireworks algorithm,FWA)的多目标优化调度模型。烟花...为了更好地满足云计算中用户的服务质量(quality of service,QoS)需求,合理利用云数据中心的资源,以任务的执行时间和虚拟机的负载均衡作为优化的目标对象,提出了一种基于烟花算法(fireworks algorithm,FWA)的多目标优化调度模型。烟花算法是一种启发式算法,利用爆炸算子、高斯变异和选择策略能较快地寻找到全局最优解。通过在Cloudsim上与粒子群优化算法(PSO)和遗传算法(GA)进行有效性和执行时间上的对比,结果表明烟花算法在不同实验次数下可持续得到最优适应度值,而且在种群规模不断扩大时,烟花算法的执行时间没有陡然增加,明显优于PSO算法和GA算法。展开更多
基金supported by the National Natural Science Foundation of China(61571149)the Special China Postdoctoral Science Foundation(2015T80325)+2 种基金the Heilongjiang Postdoctoral Fund(LBH-Z13054)the China Scholarship Council and the Fundamental Research Funds for the Central Universities(HEUCFP201772HEUCF160808)
文摘For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best cooperative mechanism(BCM)is proposed for CR with EH. To get the optimal estimation performance, a quantum fireworks algorithm(QFA) is designed to resolve the difficulties of maximal throughput and EH, and the proposed cooperative mechanism is called as QFA-BCM. The proposed QFA combines the advantages of quantum computation theory with the fireworks algorithm(FA). Thus the QFA is able to obtain the optimal solution and its convergence performance is proved. By using the new cooperation mechanism and computing algorithm, the proposed QFA-BCM method can achieve comparable maximal throughput in the new timeslot structure. Simulation results have proved that the QFA-BCM method is superior to previous non-cooperative and cooperative mechanisms.
文摘为了满足供应链物流的不同需求,考虑多种车型、车辆容量、车辆油耗、车辆最大配送距离等约束条件,以最小油耗、最短配送距离为目标,建立多车型供应链物流运输调度模型(Multi-Type Vehicle Routing Problem in Supply Chain,MTVRPSC),并提出一种混沌烟花算法求解该模型。该算法以烟花算法为核心,提出一种编解码策略实现连续空间到MTVRPSC离散空间的映射,重新定义算法的适应度函数、适应度值和适应度的比较方法,并采用混沌初始化策略和混沌搜索策略来增强算法收敛效果。实验结果表明,所提出的算法在求解MTVRPSC时具有较强的寻优能力和稳定性。
文摘为了更好地满足云计算中用户的服务质量(quality of service,QoS)需求,合理利用云数据中心的资源,以任务的执行时间和虚拟机的负载均衡作为优化的目标对象,提出了一种基于烟花算法(fireworks algorithm,FWA)的多目标优化调度模型。烟花算法是一种启发式算法,利用爆炸算子、高斯变异和选择策略能较快地寻找到全局最优解。通过在Cloudsim上与粒子群优化算法(PSO)和遗传算法(GA)进行有效性和执行时间上的对比,结果表明烟花算法在不同实验次数下可持续得到最优适应度值,而且在种群规模不断扩大时,烟花算法的执行时间没有陡然增加,明显优于PSO算法和GA算法。
