基于改进拥挤距离的多目标进化算法

针对多目标进化算法的拥挤距离截断算子的分布度保持不足以及在二进制编码情况下较难收敛的缺点,提出一种改进的多目标进化算法,使用改进的拥挤距离截断算子和自适应变异算子,与经典的多目标进化算法进行对比,实验表明,该算法得到的Pareto解集具有良好的收敛性和分布性。     

基于自适应进化学习的约束多目标粒子群优化算法

针对约束边界粒子在边界区域搜索能力不足的问题,提出一种基于自适应进化学习的约束多目标粒子群优化算法。该算法根据不符合约束条件粒子的约束违反程度,修正优化算法的进化学习公式,提高算法在约束边界区域的搜索能力;通过引入一种基于拥挤距离的Pareto最优解分布性动态维护策略,在不增加算法复杂度的前提下改进Pareto前沿的分布性。实验结果表明,所提出的算法可以获得具有更好收敛性、分布性和多样性的Pareto前沿。     

基于改进多目标骨干粒子群算法的电力系统环境经济调度

针对粒子群优化算法种群多样性不足、易陷入局部寻优的问题,提出一种基于改进多目标骨干粒子群优化算法(improved bare-bones multi-objective particle swarm optimization,IBBMOPSO)的电力系统环境经济调度的求解方法.IBBMOPSO采用一种搜索权重非线性递减...     

数据中心电费最小化的负载调度策略

为了降低电费成本,一些数据中心使用绿色能源供电。然而,负载的波动性和电价的时间差异性给数据中心电费成本带来了挑战。针对上述问题,提出一种低成本的负载调度算法,使得数据中心电费最小化。首先,建立电力耗费模型;然后,将电费最小化问题形式化为一个多目标约束的优化问题;最后,求解该优化问题得到相应的负载调度策略。实验结果表明:该算法可以在保证负载性能的前提下,有效降低数据中心的电费成本。     

基于模因优化和循环调度的多目标负载均衡技术

针对现有多目标调度方法所需时间较长以及处理突发情况时性能降低的问题,提出一种基于模因优化和循环调度的多目标负载均衡技术.使用突发检测器检测发送到云服务器的用户请求,确定负载状态.基于测器结果,应用不同的负载平衡算法来高效地调度用户任务.利用选定的负载平衡算法将用户请求任务调度到资源最佳的虚拟机上,保证在最低的时间消耗内...     

基于双重距离的多目标粒子群优化算法

多目标粒子群优化(multi-objective particle swarm optimization,MOPSO)算法在维护收敛性的同时搜索分布良好的最优解集较为费力.为此,提出一种基于双重距离的MOPSO,由种群的平均距离定义粒子的邻域空间,邻域粒子数为粒子的等级,数量越多,粒子的等级越大.当等级相同时,算法结合粒子的拥挤距离选择最优粒子,并更新外部归档集.此外,算法结合粒子的变异行为避免陷入局部最优.在对比实验中,该算法在收敛性和多样性上可取得较优结果.最后,将该算法应用到电力系统的环境/经济调度模型(environmental/economic dispatch,EED),也可获得性能较好的解集.     

基于改进粒子群优化算法的约束多目标优化

针对约束多目标优化问题,提出一种改进的粒子群优化算法,采用距离量度和自适应惩罚函数相结合的约束处理技术,通过可行解比例有效均衡目标函数和约束条件,提高算法的边界搜索能力。定义新的k最近邻聚集密度,保持解集分布性,并将聚集密度和轮盘赌选择相结合选取全局最优粒子。仿真结果表明,该算法在Pareto解集均匀性及逼近性方面均具有优势。     

基于改进NSGA-Ⅱ算法的多目标生产智能调度

在制造业自动化、智能化生产模式的需求日益增加的趋势下,针对生产制造过程中生产工序安排不合理造成的生产效率低、资源浪费严重等问题,构建以最大完成时间和最大生产成本的智能优化调度模型,使用一种改进的NSGA-Ⅱ算法进行研究.通过MSOS染色体编码方案,将个体基因分成机器和工序两部分分别编码.种群初始化通过适当扩大种群的方式...     

一种最小化绿色数据中心电费的负载调度算法

为了减少电费和碳排放,数据中心运营商开始建立就地绿色能源发电厂以进行供电.然而,负载的波动性、电价的时间差异性以及绿色能源的间歇性,给节约数据中心电费带来了挑战.针对以上问题,提出一种在线式负载调度算法,可以在不使用未来的负载、电价和绿色能源可用性信息的前提下,最小化数据中心的电费.首先,建立拥有就地绿色能源发电厂的数据中心的电费模型;然后,将数据中心电费最小化问题形式化为一个随机优化问题;最后,求解该优化问题得到相应的负载调度策略.基于真实数据的实验结果表明：该算法可以在保证负载性能的前提下,有效降低数据中心的电力成本.     

基于多班教学优化的多目标分布式混合流水车间调度

单工厂环境下的混合流水车间调度问题已受到广泛关注,而多工厂环境下的分布式混合流水车间调度问题(distributed hybrid flow shop scheduling problem,DHFSP)研究进展则较小.针对考虑顺序相关准备时间的DHFSP,提出一种多班教学优化(multi-class teaching-...     

Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks

This paper proposes a scalable, local privacy-preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization-based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.     

Energy-aware application performance management in virtualized data centers

Both performance and energy cost are important concerns for current data center operators. Traditionally, however, IT and mechanical engineers have separately optimized the cyber and physical aspects of data center operations. This paper considers both of these aspects with the eventual goal of developing performance and power management techniques that operate holistically to control the entire cyber-physical complex of data center installations. Toward this end, we propose a balance of payments model for holistic power and performance management. As an example of coordinated cyber-physical system management, the energy-aware cyber-physical system (EaCPS) uses an application controller on the cyber side to guarantee application performance, and on the physical side, it utilizes electric current-aware capacity management (CACM) to smartly place executables to reduce the energy consumption of each chassis present in a data center rack. A web application, representative of a multi-tier web site, is used to evaluate the performance of the controller on the cyber side, the CACM control on the physical side, and the holistic EaCPS methods in a mid-size instrumented data center. Results indicate that coordinated EaCPS outperforms separate cyber and physical control modules.     

Utilization-based pricing for power management and profit optimization in data centers

Traditional load balancing approaches may spread the load on more computers as long as the performance in terms of response time or cost is minimized. Nowadays power is a growing cost factor for data centers. In this paper, from the service provider’s point of view, the load balancing decision is made based on whether power consumption can be reduced or more profit can be earned. To achieve this, we design pricing algorithms to influence the load distribution. Both algorithms take into account the utilization of computers besides other factors, such as prices and power costs. In the first algorithm, we design pricing functions with respect to the computer utilization to encourage or discourage resource usage. In the second algorithm, we focus on the profit that a service provider can earn after deducting power cost from its revenue. We formulate this profit optimization problem and derive the optimum price solution.     

卡尔曼一致性滤波器的丢包性能分析及能量优化

针对无线传感器网络(wireless sensor networks,WSNs)在实际应用中不可避免的数据包丢失现象,本文研究了分布式卡尔曼一致性滤波算法(distributed Kalman consensus filtering algorithm,DKF)在两类丢包情况下的稳定性和滤波性能问题,通过矩阵论理论分析得出了估计误差协方差收敛所能容忍的极限丢包率.然后,考虑到传感器节点能量有限,基于逾渗模型构建了一种能量可调的改进型分布式一致性卡尔曼滤波器,该滤波器充分利用无线传感器节点冗余布置的特点,以较小的滤波精度下降为代价,获取网络寿命的大幅度提高,实现了该分布式滤波器在滤波精度与能量消耗两个关键指标的有效权衡.最后利用仿真实例验证了所提出算法的有效性.     

Power and performance management for parallel computations in clouds and data centers

We address scheduling independent and precedence constrained parallel tasks on multiple homogeneous processors in a data center with dynamically variable voltage and speed as combinatorial optimization problems. We consider the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint on multiple processors. Our approach is to use level-by-level scheduling algorithms to deal with precedence constraints. We use a simple system partitioning and processor allocation scheme, which always schedules as many parallel tasks as possible for simultaneous execution. We use two heuristic algorithms for scheduling independent parallel tasks in the same level, i.e., SIMPLE and GREEDY. We adopt a two-level energy/time/power allocation scheme, namely, optimal energy/time allocation among levels of tasks and equal power supply to tasks in the same level. Our approach results in significant performance improvement compared with previous algorithms in scheduling independent and precedence constrained parallel tasks.     

海量视频数据分布式存储性能优化方法研究

针对文件系统存储效率低、多余副本导致空间浪费以及磁盘能源损耗严重的问题,提出一种新型分布式优化存储策略(distributed optimized storage strategy,DOSS).首先,引入Bcache混合存储技术在磁盘阵列前增设固态硬盘,作为高速缓冲区对多路视频流进行临时数据组织,变多线程并发写任务为单线程顺序写入任务,规避磁盘内部碎片产生,有效提高系统写入效率.其次,提出改进的liberation码对视频数据进行压缩存储,在保证系统可靠性的同时提高磁盘空间利用率.最后,基于ioctl系统调用编写盘片转速控制函数,实现磁盘多级休眠和低延迟唤醒,减低磁盘能耗,提高使用寿命.结果表明单台存储服务器在500路4 Mbps并发视频流下存储效率提高约36％,存储空间节省约40％,系统应对12000路并发视频流时仍存在休眠磁盘约10％.     

Multi-objective energy management for PHEV using Pontryagin's minimum principle and particle swarm optimization online

Dear editor, Plug-in hybrid electric vehicles(PHEVs),which can be charged through an external power grid,have been regarded as one of the research areas for the...     

多目标协同正弦优化算法求解分布式流水车间调度问题

针对最小化最大完工时间(makespan)、总拖期以及平均空闲时间的多目标序列相关准备时间分布式流水车间调度问题, 本文提出一种多目标协同正弦优化算法(MCSOA). 算法主要包括4个核心阶段: 在多邻域搜索阶段,提出了基于关键工厂的搜索策略, 并通过正弦优化算法控制搜索范围; 在破坏重构阶段, 设计了一种迭代搜索策略引导个体的进化方向, 同时使用正弦优化算法平衡全局开发与局部搜索; 在选择阶段, 使用非支配排序与参考点的方法筛选优质解, 外部档案集用于存储所有非支配解; 在协同阶段, 设计种群间共享与竞争机制, 平衡3个目标的优化. 本文通过多目标优化的均匀性、反世代距离和覆盖率3项性能指标验证算法的有效性, 并使用非参数检验证明所提出的算法具有显著性优势.