用一种改进的遗传算法求解旅行商问题

本文借鉴蚁群算法的思想来对遗传算法进行改进,在改进算法中引入了多线程技术。本文用C#实现了该改进算法,并应用该算法完成了30城市旅行商问题的求解。     

一种用于自动生成二维全四边形有限元网格的改进的铺设算法

本文所述改进算法删除了原有算法的诸多繁琐之处,改进了原有算法的许多不足,并加入了一些新的算法规则。实例测试结果表明,用本文所述改进算法生产的四边形网格具有网格分布均匀,四格四边形接近于规则四边等特点。     

基于任务调度的无线网贪婪信道分配算法

针对无线网络链路干扰问题,综合借鉴多处理器任务调度算法提出了一种贪婪信道分配算法,为所访问的无线网链路甄选出干扰最小的信道,并且证明了本算法的近似比率为2-1/k,其中为k为可用的正交信道数,算法复杂度为O（|E|2）。为了验证本文算法的可行性和有效性,将本文所提出的贪婪算法与随机信道分配算法和按序信道分配算法进行了实验对比。仿真结果表明：本文所提出的贪婪算法的整体性能优于其他两种算法,并且贪婪算法得到的最大干扰和平均干扰归一化值随着可用正交信道数的变化趋势较其他两种算法稳定。从而验证了本文算法能有效的降低链路干扰,一定程度上可以提升网络吞吐量。     

融入用户-项目标注网络影响力的改进slope one算法研究

在协同过滤推荐领域中,slope one算法取得了较好的效果,但是该类算法忽略用户及项目影响力差异性。针对此处不足,本文提出融入用户-项目标注网络影响力的改进slopeone算法,该算法从用户影响力及项目热度两方面计算权重值,并融入传统算法中,达到了进一步提高算法准确率的目的。将本文算法应用在MovieLens数据集上与若干近似算法对比试验,结果显示本文算法在推荐准确率上有明显提升。     

网络拓扑发现算法综述

本文分别总结了网络物理拓扑发现算法和网络逻辑拓扑发现算法两大类拓扑发现算法的基本思想和设计方法,分析了影响各算法性能的主要因素,并对今后网络拓扑发现算法的设计路线作了展望和提出了建议。我们认为本文对网络拓扑发现算法的设计者有较高的参考价值。     

求解迷宫布线问题的人工智能图搜索产生式系统

本文提出了一个求解迷宫布线问题的人工智能图搜索产生式系统。当取不同的控制策略,本系统可分别实现Lee算法和Rubin算法。基于上述系统本文还提出了一个启发性迷宫布线过程,其搜索的面积元素比Rubin算法减少30％左右。本文最后对Lee,Rubin算法和本文提出的过程作了统计比较。     

基于粗糙集的属性约简算法

本文主要研究基于粗集理论的属性约简算法。提出了一种新的启发式约简算法,即基于加权平均和频度的双向选择约简算法。本文还通过实例验证了该算法的可行性和有效性。     

一种面向入侵检测的Wu-Manber算法研究

模式匹配算法是入侵防御系统中检测引擎的核心算法,模式匹配算法的效率决定了入侵防御系统的性能。本文对模式匹配算法进行了研究,重点分析了多模式匹配算法Wu-Manber算法,并针对Wu-Manber算法存在的不足,提出了Wu-Manber算法的改进算法。     

片上网络中面向报文有序传输的自适应路由算法研究

与确定性路由算法相比,自适应路由算法可以提高片上网络的通信性能,但是报文可能会无序到达。在目的节点对报文排序将会导致严重的面积和计算开销,甚至可能会抵消采用自适应路由算法带来的性能增益。为此,本文首先提出一种部分自适应路由算法,以满足报文的有序到达。然后,描述了对本文算法提供支持的路由器硬件结构。最后,在二维片上网络下对本文算法及确定性和自适应路由算法进行了性能评估和比较。与XY算法相比,本文算法显著降低了报文延时,提升了饱和点。同时讨论了对路由器面积和功耗影响。虽然路由器的功耗有所上升,但是由于报文交付性能提升,因此每个flit的能耗增长可忽略不计。     

一种单模式匹配算法

本文在介绍经典模式匹配算法BM算法及其改进算法BMH算法的基础上,提出了一种单模式匹配算法。实验表明,该算法有效的加快了模式匹配的速度,提高了匹配效率。     

混合型蛙跳算法及其应用研究*

为了提高蛙跳算法求解无约束连续优化问题的能力,提出了一种改进型混合蛙跳算法。为验证该算法求解函数优化问题的高效性,将其与基本蛙跳算法进行比较实验,结果表明该算法的解精度及收敛速度均优于基本蛙跳算法,更适用于求解复杂的无约束连续优化问题。     

利用Alopex改进的动力学演化算法

通过将动力学演化算法(Dynamical Evolutionary Algorithm,DEA)与一种随机优化方法——Alopex算法相结合,提出一种改进的动力学演化算法。改进的算法改善了动力学演化算法摆脱局部极小点的能力,对典型函数的测试表明:改进算法的全局搜索能力有了显著提高,特别是对多峰函数能够有效地避免早熟收敛问题。     

Sequential approximation optimization assisted particle swarm optimization for expensive problems

In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems.     

双门限和机会协作的频谱感知算法研究

针对传统频谱感知算法性能较差及一文献中Zhu所提出的算法功率消耗大的不足,提出了一种基于双门限和机会协作的频谱感知算法,同时理论推导了在瑞利衰弱信道中基于该算法的频谱感知检测概率,并对传统频谱感知算法,Zhu所提出的算法和基于双门限和机会协作的频谱感知算法进行性能仿真。仿真结果表明,该算法可以有效提高频谱感知检测概率,性能优于传统算法,与Zhu所提出的算法性能基本相同,且能有效节省发射功率。     

An efficient immersion-based watershed transform method and its prototype architecture

This paper describes an improved immersion-based watershed algorithm to compute the watershed lines for segmentation of digital gray scale images and its hardware implementation. The proposed algorithm is devoid of certain disadvantages inherent in a conventional immersion-based algorithm originally proposed by Vincent and Soille. Flooding of catchment basins from pre-determined regional minima and conditional neighborhood comparisons while processing the eight neighboring pixels of a labeled center pixel ensures thin continuous watershed lines. Reduced computational complexity and increased throughput compared to the conventional algorithm occurs from simultaneous determination of labels of various neighboring pixels. The complexity of the proposed algorithm is analyzed. The results of running both the proposed and the conventional algorithm on different test images clearly establish the superiority of the proposed algorithm. A prototype architecture designed to implement the proposed watershed algorithm has been modelled in VHDL and synthesized for Virtex FPGA. The FPGA implementation results show acceptable performance of the proposed architecture.     

Faster and more robust point symmetry-based K-means algorithm

Based on the recently published point symmetry distance (PSD) measure, this paper presents a novel PSD measure, namely symmetry similarity level (SSL) operator for K-means algorithm. Our proposed modified point symmetry-based K-means (MPSK) algorithm is more robust than the previous PSK algorithm by Su and Chou. Not only the proposed MPSK algorithm is suitable for the symmetrical intra-clusters as the PSK algorithm does, the proposed MPSK algorithm is also suitable for the symmetrical inter-clusters. In addition, two speedup strategies are presented to reduce the time required in the proposed MPSK algorithm. Experimental results demonstrate the significant execution-time improvement and the extension to the symmetrical inter-clusters of the proposed MPSK algorithm when compared to the previous PSK algorithm.     

基于Spark的并行社区发现算法

针对大规模图数据顶点聚类进行研究,提出了一种基于Spark的并行社区发现算法,其在基于极值优化的串行社区发现算法的基础上设计而成。此外还针对该串行算法在簇调整时因选择顶点数量过少而影响算法运行效率的问题,提出了一种多个顶点选择方法。该方法会计算一个阈值并发现所有适应度值小于该阈值的顶点,作为被选择的顶点;由于阈值是基于所有顶点的适应度值计算出来的,为了避免非常大的适应度值对阈值造成的影响该方法会限制被选择顶点的数量,若被选择的顶点过多,算法只保留其中的一部分。同时,还提出了一种顶点过滤方法,其可以有效减少图数据的数据量。在实验当中,提出算法的运行时间明显短于比较的其他基于Spark的并行化社区发现算法,可以发现提出算法的运行速度相对较快。     

基于正则表达式的深度包检测算法

在深入分析了DFA状态数对算法性能影响的基础上,提出了一种新的基于正则表达式的深度包检测算法,该算法保证在任意有限的系统资源下算法的时间复杂度空间复杂度最小。在Linux下实现了该算法,并对基于L7-filter模式集合的网络数据包进行了大量检测实验。结果表明,与已有的正则表达式算法比较,该算法的时间复杂度和空降复杂度最小。     

Multi-Agent Consensus Using Both Current and Outdated States with Fixed and Undirected Interaction

We propose a distributed consensus algorithm for multi-agent systems. In contrast to the standard consensus algorithm that relies on only current states, the proposed algorithm uses both current states and outdated states stored in memory. The proposed algorithm is analyzed under an undirected communication graph. It is shown that the proposed algorithm converges faster than the standard consensus algorithm while requiring identical maximum control effort if the outdated states are chosen properly. Simulation results demonstrate the effectiveness of the proposed algorithm.     

A high performance genetic algorithm using bacterial conjugation operator (HPGA)

In this paper an efficient evolutionary algorithm is proposed which could be applied to real-time problems such as robotics applications. The only parameter of the proposed algorithm is the “Population Size” which makes the proposed algorithm similar to parameter-less algorithms, and the only operator applied during the algorithm execution is the bacterial conjugation operator, which makes using and implementation of the proposed algorithm much easier. The procedure of the bacterial conjugation operator used in this algorithm is different from operators of the same name previously used in other evolutionary algorithms such as the pseudo bacterial genetic algorithm or the microbial genetic algorithm. For a collection of 23 benchmark functions and some other well-known optimization problems, the experimental results show that the proposed algorithm has better performance when compared to particle swarm optimization and a simple genetic algorithm.