基于改进小生境遗传算法的微波/光混合链路中继卫星资源调度方法

针对微波/光混合链路中继卫星系统多资源约束下的多目标综合调度问题,分析了微波与激光混合链路的主要特点和影响因素,建立了混合链路资源调度多目标约束规划模型;将小生境技术引入遗传算法,并设计了基于精英保留的选择机制和自适应的交叉、变异算子,提出了一种改进的小生境遗传算法对模型进行求解。所提算法可有效避免遗传算法局部优化能力差及容易陷入局部最优等缺陷,同时能够防止最优解的丢失,解决了混合链路多资源约束下的多目标综合调度问题。仿真结果表明,相对与传统的遗传算法,本文算法在保持种群多样性和求解全局最优解方面具有优势,有效解决微波/激光混合链路中继卫星系统的多目标综合调度问题。     

基于改进小生境遗传算法的微波/光混合链路中继卫星资源调度方法

针对微波/光混合链路中继卫星系统多资源约束 下的多目标综合调度问题,分析了微波与激光混 合链路的主要特点和影响因素,建立了混合链路资源调度多目标约束规划模型;将小生境技 术引入遗传 算法,并设计了基于精英保留的选择机制和自适应的交叉、变异算子,提出了一种改进的小 生境遗传算 法对模型进行求解。所提算法可有效避免遗传算法局部优化能力差及容易陷入局部最优等缺 陷,同时能够 防止最优解的丢失,解决了混合链路多资源约束下的多目标综合调度问题。仿真结果表明 ,相对与传统 的遗传算法,本文算法在保持种群多样性和求解全局最优解方面具有优势,有效解决微波/ 激光混合链路中继卫星系统的多目标综合调度问题。     

复杂真实约束条件下的多星测控资源调度

测控资源调度是卫星调度研究领域的关键问题之一,其在多星条件下面临着因约束信息来源不同、结构差异大、表达模糊而难以建立统一的约束模型,以及求调度问题的最优解是NP-hard的且不易得到较优解的问题。为此,首先将多星测控问题表达为组合优化模型,再将复杂的约束信息归纳为一套约束,最后提出了一种双层并行约束匹配算法求解问题。与用户现有算法对比,所提算法的周期测控调度成功率提升了9%左右,且可以处理更多约束信息类型。     

面向密集多波束组网的卫星通信系统资源调度算法

研究了高动态、资源受限条件下的卫星通信系统资源调度问题。以时间窗口、卫星功耗、信道数量、用户优先级以及任务突发性为约束,建立了卫星资源调度模型。考虑到传统的蚁群优化算法存在初期搜索速度过慢、局部搜索能力较弱以及易陷入局部最优等缺点,提出了以初始解集构造、额外信息素沉积为核心的改进蚁群优化算法,来求解资源调度问题。仿真实验评估了所提资源调度算法在完成任务的数量、优先级和调度完成时间方面的性能。实验结果表明,所提算法具有较快的收敛速度,且与同类型优化算法相比具有更高的调度效率,适用于调度面向密集多波束组网需求的卫星通信系统资源。     

基于改进粒子群算法的天基预警系统资源调度方法

天基预警系统资源调度是一项重要而棘手的问题。对预警任务特性进行了分析,在此基础上提出一种基于关键点的任务分解方法,将其转换为可求解的组合优化问题;建立了问题的约束满足模型。针对该模型规模大、变量多的特点,设计一种具有快速求解能力的改进粒子群算法进行求解,该算法采取早熟避免机制,防止粒子群算法易产生的早熟现象。实验结果表明算法能够在给定时间内求得理想的调度方案。     

行为综合功能流水线中的负载平衡蚁群调度算法

在专用集成电路高层次综合中,功能流水线是提高算法描述执行速度的关键技术.针对时间约束和资源约束的两类行为综合功能流水线调度问题,提出了一种基于蚁群优化(ACO)的调度算法.LB-ACO算法将ACO算法与力向算法相结合,使用修改的力向公式定义局部试探因子,用个体调度结果的质量来更新全局试探因子.实验结果表明,LB-ACO算法在保证较低的时间复杂度O(cn2)的前提下,获得接近最优的调度结果.     

应用混合遗传算法求解飞机牵引调度问题

空军飞行训练中飞机牵引调度是一个并行的模糊Job-Shop调度.为解决此NP难问题,融合模拟退火和遗传算法二者优势,进行了混合遗传算法的分析和仿真运算.仿真结果与实际飞行现场的调度比较表明,在同样资源约束下本算法可缩短飞行调度时间约20%以上,或在同样飞行调度时间条件下可减少牵引车占用率20%以上.     

虚拟工作流约束的时间-精确率迭代规约优化算法

针对复杂产品生产业务调度这一问题,该文运用工作流技术并以完工时间为约束,提出一种虚拟迭代归约算法,能较好地在完工时间约束下优化生产精确率。通过将各制约任务抽象虚拟成一个虚拟节点,采用逆向迭代的求解方式,确定了一条兼顾完工时间与生产精确率的调度路径。对比发现,虚拟迭代归约算法对全局生产精确率有较大幅度的提高,且通过改变截止期、任务数等参数可以提高算法的效率。     

基于粒子群优化的地震应急物资多目标调度算法

合理高效地优化调度救灾物资对提升地震应急救援效果具有重要意义。地震应急需要同时兼顾时效性、公平性和经济性等相互冲突的多个调度目标。该文对地震应急物资调度问题建立了带约束的3目标优化模型，并设计了基于进化状态评估的自适应多目标粒子群优化算法(AMOPSO/ESE)来求解Pareto最优解集。然后根据“先粗后精”的决策行为模式提出了由兴趣最优解集和邻域最优解集构成的Pareto前沿来辅助决策过程。仿真表明该算法能有效地获得优化调度方案，与其他算法相比，所得Pareto解集在收敛性和多样性上具有性能优势。     

基于寻优算法的智能RGV的动态调度问题

文章主要研究了智能加工系统中RGV调度决策的优化问题,将CNC工作时间与系统运行时间之比作为衡量系统工作效率的指标,对于一道工序的物料加工作业情况,可以利用模拟退火算法和MATLAB软件得到在下RGV的调度模型和求解算法;对于两道工序的物料加工作业情况,可以利用遗传算法和MATLAB软件求解出CNC的最优布置与RGV调度模型和求解算法。     

A near-optimal sensor placement algorithm to achieve complete coverage-discrimination in sensor networks

In this letter, we develop a robust and scalable algorithm to cope with the sensor placement problem for target location under constraints of the cost limitation and the complete coverage. The problem is NP-complete for arbitrary sensor fields. The grid-based placement scenario is adopted and the sensor placement problem formulated as a combinatorial optimization problem for minimizing the maximum distance error in a sensor field under the constraints. The proposed algorithm is based on the simulated annealing approach. The experimental results reveal that, for small sensor fields, the algorithm can find the optimal sensor placement under the minimum cost limitation. Moreover, it can also find a placement with minimum distance error for large sensor fields under the cost limitation.     

Design of optimal stack filters under the MAE criterion

The design of optimal stack filters under the MAE criterion is addressed in this paper. In our work, the Hasse diagram is adopted to represent the positive Boolean functions to solve the optimization problem. After problem transformation, the finding of the optimal stack filter is equivalent to the finding of the optimal on-set such that the total cost of the on-set is minimal. An efficient algorithm is developed that makes use of an important property of the optimal on-set to avoid fruitless search. It thereby greatly reduces the complexity in finding the corresponding optimal stack filter. A design example is illustrated in detail to demonstrate the optimization procedures. The proposed algorithm can generate the optimal stack filter in 1 s for the window size of 14 pixels. It can still generate the optimal stack filter for the window size of 21, although it takes about 4 h. Experimental results for real images reveal that the proposed algorithm essentially extends the maximum filter window size to make the stack filter optimization problem computationally tractable     

Quantization of FIR filters under a total integer cost constraint

In this paper, we present computationally efficient algorithms for obtaining a particular class of optimal quantized representations of finite-impulse response (FIR) filters. We consider a scenario where each quantization level is associated with a certain integer cost and, given an FIR filter with real coefficients, our goal is to find the quantized representation that minimizes a certain error criterion under a constraint on the total cost of all quantization levels used to represent the filter coefficients. We first formulate the problem as a constrained shortest path problem and discuss how an efficient dynamic programming algorithm can be used to obtain the optimal quantized representation for arbitrary quantization sets. We then develop a greedy algorithm which has even lower computational complexity and is shown to be optimal when the quantization levels and their associated costs satisfy a certain, easily checkable criterion. For the special case of the quantization set that involves levels that are sums of signed powers-of-two and whose cost is captured by the number of powers of two used in their representation, the total integer cost relates to the cost of the very large-scale integration implementation of the given FIR filter and our analysis clarifies the optimality of previously proposed algorithms in this setting.     

Minimum-cost QoS multicast and unicast routing in communication networks

In this paper, we study the minimum-cost quality-of-service multicast and unicast routing problems in communication networks. For the multicast problem, we present an efficient approximation algorithm to find a balance between a minimum-cost multicast tree and a minimum-delay multicast tree, with a provably good performance under the condition that link delay and link cost are identical. For the unicast problem, we present an efficient primal-dual heuristic algorithm to find a path which balances path cost and path delay, together with an error bound. The lack of a provably good performance for the second algorithm is complemented by computational results on randomly generated networks. Our algorithm finds optimal solutions in more than 80% of the cases and finds close to optimal solutions in all other cases, while using much less time.     

McPAO: A Distributed Multi-channel Power Allocation and Optimization Algorithm for Femtocells

Efficient radio resource management is a key issue in a multi-channel femtocell system, where femtocell base stations are deployed randomly and will generate interference to each other. In this research, we formulate multi-channel power allocation as a convex optimization problem, in order to maximize the overall system throughput under complex transmit power constraint. We apply the Lagrangian duality techniques to make the problem decomposable and propose a distributed iterative subgradient algorithm, namely Multi-channel Power Allocation and Optimization (McPAO). Specifically, McPAO consists of two phases: (I) a gradient projection algorithm to solve the optimal power allocation for each channel under a fixed Lagrangian dual cost; and (II) a subgradient algorithm to update the Lagrangian dual cost by using the power allocation results from Phase I. This two-phase iteration process continues until the Lagrangian dual cost converges to the optimal value. Numerical results show that our McPAO algorithm can improve the overall system throughput by 18?%, comparing to with fixed power allocation schemes. In addition, we study the impact of errors in gradient direction estimation (Phase I), which are caused by limited or delayed information exchange among femtocells in realistic situations. These errors will be propagated into the subgradient algorithm (Phase II) and, subsequently, affect the overall performance of McPAO. A rigorous analytical approach is developed to prove that McPAO can always achieve a bounded overall throughput performance very close to the global optimum.     

基于实数型遗传算法的电子系统可靠性最优分配

本文根据电子系统中最小成本问题和最大可靠性问题各自的特点 ,提出了用实数型遗传算法求解有约束的非线性最优化问题的方案。数值计算表明 ,实数型遗传算法在求解电子系统可靠性最优分配问题上能获得比传统的优化方法更好的结果。     

System Availability and Optimum Spare Units

The steady-state availability of a repairable system with cold standbys and nonzero replacement time is maximized under constraints of total cost and total weight. Likewise the cost can be minimized under constraints of steady-state availability and total weight. A new, more efficient algorithm is used for the constrained optimization. The problem is formulated as a nonlinear integer programming problem. Since the objective functions are monotone, it is easy to obtain optimal solutions. These new algorithms are natural extensions of the Lawler-Bell algorithm. Availability is adjusted by the number of spares allowed. Other measures of system goodness are considered, viz, failure rate, weight, price, mean repair time, mean repair cost, mean replacement time, and mean replacement cost of a unit.     

基于深度强化学习的云边协同计算迁移研究

基于单一边缘节点计算、存储资源的有限性及大数据场景对高效计算服务的需求,本文提出了一种基于深度强化学习的云边协同计算迁移机制.具体地,基于计算资源、带宽和迁移决策的综合性考量,构建了一个最小化所有用户任务执行延迟与能耗权重和的优化问题.基于该优化问题提出了一个异步云边协同的深度强化学习算法,该算法充分利用了云边双方的计...     

Mobility aware edge service migration strategy

To address the problem of load imbalance among edge servers and quality of service degradation caused by dynamic changes of user locations in mobile edge computing networks,a mobility aware edge service migration algorithm was proposed.Firstly,the optimization problem was formulated as a mix integer nonlinear programming problem,with the goal of minimizing the perceived delay of user service request.Then,the delay optimization problem was decoupled into the edge service migration and edge node selection sub-problems based on the Lyapunov optimization approach.Thereafter,the fast edge decision algorithm was proposed to optimize the resource allocation and edge service migration under a given radio access strategy.Finally,the asynchronous optimal response algorithm was proposed to iterate out the optimal radio access strategy.Simulation results validate the proposed algorithm can reduce the perceived delay under the service migration cost constraint while comparing with other existing algorithms.     

一种基于遗传算法的SVM决策树多分类方法

在当前的机器学习领域,如何利用支持向量机（SVM）对多类目标进行分类,同时提高分类器的分类效率已经成为研究的热点之一,有效地解决此问题对于提高目标的识别概率具有较大意义。本文针对SVM多分类问题提出了一种基于遗传算法的SVM最优决策树生成算法。算法以随机生成的决策树构建的SVM分类器对同一测试样本的分类正确率作为遗传算法的适应度函数,通过遗传算法寻找到最优决策树,再以最优决策树构建SVM分类器,最终实现SVM的多分类。将该算法应用于低空飞行声目标识别问题,实验结果表明,新方法比传统的1-a-1、1-a-r、SVM-DL和GADT-SVM方法有更高的分类精度和更短的分类时间。