共查询到18条相似文献,搜索用时 610 毫秒
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基于粒子群算法的认知无线电频谱分配算法 总被引:3,自引:0,他引:3
针对认知无线电空闲频谱分配过程中整体性能优化问题,建立了频谱资源受限情况下实现系统总带宽收益最大化、认知用户接入公平性最优的多目标模型,并结合问题特点设计了基于粒子群优化算法的智能求解算法,给出了具体的实施步骤。从系统总带宽收益、用户接入公平性和系统整体性能3个方面,仿真比较分析了所提算法同协作最大化带宽总收益和协作最大化比例公平性准则下的敏感图着色算法的性能,结果表明该方法实现了系统总带宽收益和用户公平性的折中,整体性能优于敏感图着色算法。 相似文献
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针对认知无线网络频谱分配过程存在的问题,提出了基于适应值预测策略的双人工蜂群算法(FP-DABC)。该算法设计的干扰门限阈值,提高了用户的接入数量;适应值预测方法的使用,加快了分配效率;同时算法对频谱分配过程公平性和系统整体性能进行了优化。实验仿真结果表明:FP-DABC算法牺牲了部分网络效益的同时,在用户满意度、分配率、平均分配时间、用户公平性和系统整体性能上均优于颜色敏感图着色算法(CSGC)和人工蜂群算法(ABC)。 相似文献
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为了解决认知无线网络中的频谱分配问题,提出一种基于多种群进化与粒子群优化混合的频谱分配算法。它采用图论着色模型,首先使用遗传算法将多个种群进行独立进化,以提高种群的全局搜索能力;然后选出每个种群中的最优的个体作为粒子群优化的粒子,并通过控制每个粒子的初始速度方向来加快算法的收敛速度。最后以系统总收益最大化和用户间的公平性为优化目标与遗传算法和粒子群算法进行了对比实验,仿真结果表明,该算法在收敛速度、认知用户接入公平性和系统总收益3个方面的性能均优于遗传算法和粒子群算法。 相似文献
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王国强 《数字社区&智能家居》2011,(7X):5207-5211
认知无线电的空闲频谱分配是一个复杂的最优化问题,需要在最大化频谱利用率的同时考虑干扰的最小化和接入的公平性。现有的CSGC模型将空闲频谱分配问题简化为一个图着色问题。针对CSGC模型"没有考虑到二级用户接收端的位置"和"没有考虑二级用户的服务时间长短"的两个缺点,结合传统的多机调度算法,提出了一种新的空闲频谱分配算法CRSAMMS。CRSAMMS不仅克服了CSGC模型的两个缺点,并且可以根据具体的应用选择不同的算法规则以进一步提高性能。Matlab仿真实验证明了CRSAMMS算法的正确性和有效性。 相似文献
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基于用户间公平性的改进型频谱分配算法 总被引:1,自引:0,他引:1
针对目前频谱资源紧缺的现状,通过对图论着色模型的分析理解,提出了一种基于用户公平性的改进颜色敏感度的图论着色算法,该算法从用户的网络效益和使用频谱数出发,引入公平因子,改变频谱分配过程中给用户的分配优先级,保证频谱分配的公平性.通过仿真表明其可行性. 相似文献
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频谱分配是认知无线电中的重要问题,而传统的频谱分配算法并未考虑频谱的差异性。提出一种基于免疫克隆优化算法、考虑频谱差异性的频谱分配算法,算法引入可信度矩阵对频谱的时间差异性进行建模。进行约束处理时,通过差异性算子(DCSO)的使用能将可信度更高的频谱分配给认知用户,从而提高系统的总收益。对于冲突激烈的认知用户,使用公平性算子(FCSO)能够增加它们被分配频谱资源的可能性,从而提高系统的公平性效益。仿真实验表明,相较于传统的免疫克隆优化算法、颜色敏感算法和遗传算法,本算法能显著增加网络的总收益、可信度,提高网络的公平性。 相似文献
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指针分析是数据流分析中的关键性技术,其分析结果是编译优化和程序变换的基础。在基于包含的指针分析算法研究的基础上,对 Narse 优先权约束评估算法中存在的冗余约束评估和优先权评估模型计算开销较大的问题进行分析,以指针的指向集更新信息确定约束评估的候选集,提出了基于指向更新的约束评估算法。采用约束语句间的解,引用依赖和标量依赖构建约束依赖图,通过依赖关系确定约束评估的优先权,提出了基于约束依赖图的优先权算法,简化了既有算法中复杂的优先权评估模型,进一步给出了优化后算法的整体框架。在基准测试集 SPEC 2000/SPEC 2006上进行实验,其结果表明,该算法与Narse优先权算法相比,在时间开销和存储开销上都有明显的性能提升。 相似文献
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This paper considers a location system where a number of deployed sensor nodes collaborate with objects that need to be localized. Unlike existing works, we focus on reducing the energy consumption of the sensor nodes, which are assumed to be static and run on limited battery power. To minimize the total wake-up time of the sensor nodes, we control the transmission schedule of each object. Because it is difficult to find an optimal solution to the considered optimization problem, we consider an approach to this problem that consists of two steps: (1) create an equivalent modified graph coloring subproblem, and (2) permute the coloring result to obtain a best possible solution. We adopt some existing graph coloring algorithms for step 1 and find two properties of optimal schedules that can be used to confine the search space for step 2. Additionally, we propose a heuristic algorithm that aims at significantly reducing the complexity for the case where the confined search space is still too large. The performance of our heuristic algorithm is evaluated through extensive simulations. It is shown that its performance is comparable to that of the simulated annealing algorithm, which gives a near-optimal solution. 相似文献
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The fireworks algorithm features a small number of parameters, remarkable optimization ability, and resistance to a local optimum. Based on the graph
coloring model, the fireworks algorithm is introduced for the first time to solve the spectrum allocation problem for cognitive radio networks, thus maximizing
utility and fairness of spectrum allocation. Two-layer binary coding is adopted for individual fireworks. The first layer refers to the coding of cognitive users
used to determine channels that can be connected with the user. The second layer refers to the auxiliary coding of channels responsible for addressing
mutual interference among multiple cognitive users when they connect with the same channel at the same time. Explosion operator, mutation operator,
and the selection operation are designed to allocate the spectrum for the cognitive radio network. Simulation results demonstrate superiority and efficiency
of the proposed algorithm in terms of spectrum allocation. 相似文献
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Javad Akbari Torkestani 《控制论与系统》2013,44(5):444-466
The vertex coloring problem is a well-known classical optimization problem in graph theory in which a color is assigned to each vertex of the graph in such a way that no two adjacent vertices have the same color. The minimum vertex coloring problem is known to be an NP-hard problem in an arbitrary graph, and a host of approximation solutions are available. In this article, a learning automata–based approximation algorithm is proposed to solve the minimum vertex coloring problem. The proposed algorithm iteratively finds the different possible colorings of the graph and compares it at each stage with the best coloring found so far. If the number of distinct colors in the chosen coloring is less than that of the best coloring, the chosen coloring is rewarded; otherwise, it is penalized. Convergence of the proposed algorithm to the optimal solution is proven. The proposed vertex coloring algorithm is compared with the well-known coloring techniques and the results show the superiority of the proposed algorithm over the others both in terms of the color set size and running time of algorithm. 相似文献