共查询到19条相似文献,搜索用时 375 毫秒
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原始的二进制粒子算法在认知无线电频谱分配过程中存在收敛速度慢、最优值不理想的问题,并且对二进制粒子群算法在频谱分配过程中的惯性权重参数设置缺乏理论研究。针对这一问题,在分析二进制粒子群算法的基本原理和计算步骤的基础上,探讨了二进制粒子群算法ω最适宜的取值。对原始二进制粒子群算法进行了改进,提出了采用混沌Logistic映射的方式进行初始种群的构造,保证了初始种群在解空间的均匀分布。引入了基于种群聚集度的粒子变异思想,使得新算法与原算法相比,不易陷入局部最优解。仿真结果表明,利用该算法进行认知无线电频谱分配时,收敛速度较快,得到的系统总效益值也较好。 相似文献
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针对LEA低C功H算法的簇头分布不合理、网络能耗不均的问题,在耗自适应集簇分层路由算法。首先将分割线的选择LE问A题CH转协议的基础上,提出了一种改进粒子群算法优化化成带约束的非线性优化问题,然后利用粒子群算法求解,并针对粒子群算法的不足进行相应的改进,最后采用仿真实验测试算法的性能。仿真结果表明,相对于其它改进LEACH路由算法,本文算法有效提高了网络的能量利用率,能够实现节点之间的能耗均衡,使无线传感器的网络生存时间得到延长。 相似文献
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为了解决铁路监测场景中线性无线传感器网络的节点间能耗不均衡导致的网络生命周期短、数据传输时延大的问题,提出了一种基于粒子群优化理论和广度优先搜索的路由算法。以候选簇头节点的相对能耗、簇头间距和簇头负载为指标构建适应度函数,通过调整惯性权重系数增强粒子群算法局部搜索能力,获得簇头最优解集;构建能耗与时延驱动的路径成本函数,基于广度优先搜索获得源节点到sink节点的最优主路径;设计基于Markov决策过程(MDP)模型的Q-learning备选路径更新与路由维护机制。仿真结果表明,所提算法能够有效均衡节点间能耗,在延长网络生命周期和降低数据传输时延方面具有较优的性能。 相似文献
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在多输入多输出系统中,发射端和接收端的多天线配置提高了信道容量和传输可靠性,而天线选择技术能在保持系统优点的同时有效地降低运算复杂度以及硬件成本。为了能在时变的信道条件下快速地选择出一组最优的天线子集,提出了一种基于二进制粒子群算法的改进的天线选择算法。推导出了二进制粒子群联合收发端天线选择的信道容量公式,并将其作为粒子群算法的适应度函数,使天线选择问题转换成二进制编码串的组合优化问题。通过改进模糊函数提高粒子群算法的收敛性,让二进制粒子群尽可能地收敛于全局最优位置。仿真结果表明,改进的算法能在降低运算复杂度的同时提高收敛性,且系统信道容量趋近于最优算法。 相似文献
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为了在文本数据中选择有效的文本特征,本文提出一种新的基于改进二进制粒子群优化的特征选择算法,该算法利用翻转角度,局部翻转因子和全局翻转因子来决定粒子群的进化,通过求解目标函数的最优解,得到二进制特征选择系数,选择特征选择系数为1的特征为有效特征。实验证明,该方法不仅有效地降低了运算开销,而且提高了文本分类的准确度。 相似文献
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为了提高列车在运行过程中的能源利用率,采用速度变异的粒子群算法,对列车运行控制策略进行优化.根据列车运行的机械能理论和能耗理论,建立了以能耗为目标,满足距离、时间和速度约束的列车运行仿真模型,在确定的工况序列条件下,求解列车最优控制策略.改进粒子群算法按照一定的概率,对最小速度进行变异,拓展了粒子有效搜索空间,从而改善优化结果.算例仿真验证了该方法的有效性和合理性. 相似文献
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5G中终端的能量消耗和频谱资源问题日益严重,在终端直通技术(D2D)中尤为突出.为了提高D2D对用户(DP)的能效和资源利用率,提出了一种基于粒子群算法的联合功率控制和资源分配策略.以最大化D2 D链路总能效为目标,将构造的资源分配矩阵和功率分配矩阵作为粒子的位置,依照蜂窝用户(CU)和DP服务质量的约束来修正粒子位置和速度,使之适合于原分式规划问题的求解,合理地提升了DP的总能效,实现了一个DP链路能复用多个CU资源.仿真结果表明,所提算法不仅使能效显著提升,而且使资源利用率提高了80%. 相似文献
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A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing 下载免费PDF全文
J. Venkata Krishna G. Apparao Naidu Niraj Upadhayaya 《International Journal of Communication Systems》2018,31(8)
The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09. 相似文献
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为了辨识压电陶瓷中的迟滞非线性,该文提出一种改进的粒子群算法(PSO)对非对称BoucWen模型进行参数优化。首先在归一化BoucWen模型中引入非对称因子描述非对称特性,解决该模型只适用于描述对称迟滞的问题。其次通过引入混沌映射、收缩因子和动态学习因子来对传统PSO进行改进,动态改变粒子群的权重和学习因子,有效地提高算法的搜索能力和收敛速度。最后通过改进的PSO对非对称BoucWen模型进行参数辨识。结果表明,改进的粒子群算法能较好地辨识BoucWen模型参数,验证了方法的有效性。 相似文献
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贾冀婷 《微电子学与计算机》2011,28(12):68-71
针对软件可靠性分配中不易求解全局最优解这一问题,将可靠性指标分配到每个模块中,并利用改进的粒子群优化算法来搜索模型的最优解.实验结果表明,改进的粒子群优化算法在求解软件可靠性分配问题时的效果优于遗传算法等其他智能优化算法. 相似文献
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针对无线传感器网络任务调度的实时性及节点计算及能量受限的特点,根据任务截止期赋予任务优先级,优先考虑高优先级任务,设计了一个无线传感器网络中带复杂联盟的自适应任务分配算法。为尽最大努力确保任务在截止期前完成,对截止期较为紧迫的任务采用历史信息生成历史联盟,并执行快速子任务分配算法;而对截止期较为宽裕的任务,在满足任务截止期约束条件下,以节点能耗和网络能量分布平衡为优化目标,采用矩阵的二进制编码形式,设计了一种离散粒子群优化算法以并行生成联盟,并执行基于负载和能量平衡的子任务分配算法。仿真实验结果表明所构造的自适应算法是有效的,在局部求解与全局探索之间能够取得较好的平衡,并能够在较短的时间内取得满意解。 相似文献
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Compared to 2D NoC, 3D NoC has better integrated density and system performance, which was a reliable method to solve the problem about low-power mapping. On the basis of the traditional particle swarm optimization algo-rithm (PSOA), a dynamic adaptive discrete particle swarm optimization algorithm (DADPSOA) was proposed . Parame-ter in this algorithm was adjusted dynamically based on the degree of early convergence and the charge of individual adap-tive value to approach the optimal solution. At the same time, the reasonable structure of the particles was made aiming at reducing the time complexity of this algorithm. Experimental results show that comparing with the random mapping, genetic algorithm (GA), PSOA and dynamic ant colony algorithm (DACA), DADPSOA can save the execution time, reduce the communication power consumption of mapping results. The power consumption of the task graph is reduced. 相似文献
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Aiming at the problem that the location distribution of cluster head nodes filtered by wireless sensor network clustering routing protocol was unbalanced and the data transmission path of forwarding nodes was unreasonable,which would increase the energy consumption of nodes and shorten the network life cycle,a clustering routing protocol based on improved particle swarm optimization algorithm was proposed.In the process of cluster head election,a new fitness function was established by defining the energy factor and position equalization factor of the node,the better candidate cluster head node was evaluated and selected,the position update speed of the candidate cluster head nodes was adjusted by the optimized update learning factor,the local search and speeded up the convergence of the global search was expanded.According to the distance between the forwarding node and the base station,the single-hop or multi-hop transmission mode was adopted,and a multi-hop method was designed based on the minimum spanning tree to select an optimal multi-hop path for the data transmission of the forwarding node.Simulation results show that the clustering routing protocol based on improved particle swarm optimization algorithm can elect cluster head nodes and forwarding nodes with more balanced energy and location,which shortened the communication distance of the network.The energy consumption of nodes is lower and more balanced,effectively extending the network life cycle. 相似文献