Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems

Evolutionary algorithms (EAs) are often employed to multiobjective optimization, because they process an entire population of solutions which can be used as an approximation of the Pareto front of the tackled problem. It is a common practice to couple local search with evolutionary algorithms, especially in the context of combinatorial optimization. In this paper a new local search method is proposed that utilizes the knowledge concerning promising search directions. The proposed method can be used as a general framework and combined with many methods of iterating over a neighbourhood of an initial solution as well as various decomposition approaches. In the experiments the proposed local search method was used with an EA and tested on 2-, 3- and 4-objective versions of two well-known combinatorial optimization problems: the travelling salesman problem (TSP) and the quadratic assignment problem (QAP). For comparison two well-known local search methods, one based on Pareto dominance and the other based on decomposition, were used with the same EA. The results show that the EA coupled with the directional local search yields better results than the same EA coupled with any of the two reference methods on both the TSP and QAP problems.     

一种高效强K—栅栏覆盖构建算法

K－栅栏覆盖是无线传感器网络覆盖控制的研究热点之一。本文构建了强栅栏覆盖模型,提出了分区强K－栅栏覆盖构建算法PMNSB,用最少的节点形成强栅栏。首先把监控区域分成多个子区域,通过匈牙利算法选用移动距离之和最少的网格集合为基准1－栅栏覆盖,缺少移动节点的子区域,选择附近区域的剩余移动节点修补形成1－栅栏覆盖。水平相邻的两个子区域之间构建竖直栅栏,这些1－栅栏合起来构成强K－栅栏覆盖。仿真结果证明了该方法的有效性,本文的研究对提升无线传感器网络的性能具有重要的理论与实际意义。     

基于IGHS算法的分段系统鲁棒极点配置优化

针对分段线性控制系统鲁棒极点配置问题,利用IGHS算法对特征向量矩阵条件数优化具有较好的收敛性,限制了特征向量选择的自由度.优化后的控制器使闭环系统矩阵允许有更大的摄动或不确定性.与已有方法相比,本文方法对条件数优化计算既不需要考虑梯度和二次可微信息,也不需要对条件数进行凸转化处理.最后,仿真实验结果验证了本方法的有效性.     

Uplink resource allocation in OFDMA system using distributed antennas

In this article, we investigate uplink resource allocation in orthogonal frequency division multiple access （OFDMA） systems that use distributed antennas. The multiple distributed receiving antennas motivate us to exploit the spatial diversity to maximize the achievable rate of the system. Given constraints on the subcarrier allocation and the power limit of each user equipment （UE）, a mixed-integer optimization problem is formulated. We first propose a centralized greedy algorithm to find the maximum achievable rate of the system. Then a heuristic algorithm is proposed that can be implemented in a distributed manner. Simulation results show that the heuristic algorithm can approximately achieve the maximum achievable rate of the system and result in significant system performance improvement over the approach assigning one user to each subcarrier.     

最大最小蚁群算法在实际GSM系统中频率规划的应用

探讨了应用最大最小蚁群算法（MMAS）来解决移动通讯系统（GSM）的频率规划问题。基于MMAS提出了一个可方便地控制算法的探索和开发能力并使之平衡的参数。实验结果表明,通过优化该参数,显著提高了算法的性能。     

利用特征点序列截断的图像匹配判决算法

当图像中特征点缺失比较严重时,利用特征点S维分配算法和K均值聚类算法的图像匹配性能较差。此外,随着待匹配图像数量的增加,上述两类算法的计算量急剧上升。针对上述问题,提出一种新的图像匹配判决算法——利用特征点序列截断的匹配判决。该算法利用了匹配特征点之间的匹配度量大于非匹配特征点之间的匹配度量,以及同一匹配簇中来源于同一图像的特征点不超过一个的先验知识,一方面对特征点缺失具有较强的鲁棒性,另一方面克服了上述两类算法中的组合爆炸问题。仿真实验证实了所提算法的有效性。     

基于决策级融合的图像传感系统目标交接

目标交接是图像传感系统中的关键技术,针对基于特征融合目标交接算法中特征提取的不准确,提出基于决策级融合的多摄像机目标交接算法。将目标的每个特征作为一个证据,给出每个证据基本置信指派函数的构造方式。利用Dempster组合规则得到融合多个证据后的基本置信指派。利用融合决策规则来判断当前目标是否和辨识框架中的某个目标为同一个目标,完成目标交接,并给出目标在系统中唯一标识。多种场景仿真实验表明该算法可以准确地完成多摄像机目标交接。     

大规模网络拓扑生成技术研究

网络实验环境对于协议和应用的开发具有十分重要的意义,大规模网络拓扑生成技术是构建网络实验环境的关键技术。本文提出了一种新的大规模网络拓扑生成框架VLTGF,该框架将网络拓扑结构分为三层,每一层分别采用不同的拓扑生成方法,并提供了可选的带宽分配机制,使得生成的网络拓扑结构更为完整和准确。     

Collaborative assignment using belief-desire-intention agent modeling and negotiation with speedup strategies

In this paper, we propose a distributed agent model that applies belief-desire-intention (BDI) reasoning and negotiation for addressing the linear assignment problem (LAP) collaboratively. In resource allocation, LAP is viewed as seeking a concurrent allocation of one different resource for every task to optimize a linear sum objective function. The proposed model provides a basic agent-based foundation needed for efficient resource allocation in a distributed environment. A distributed agent algorithm that has been developed based on the BDI negotiation model is examined both analytically and experimentally. To improve performance in terms of average negotiation speed and solution quality, two initialization heuristics and two different reasoning control strategies are applied, with the latter yielding different variants of the basic algorithm. Extensive simulations suggest that all the heuristic-algorithm combinations can produce a near optimal solution soon enough in some specific sense. The significance and applicability of the research work are also discussed.     

Dynamic analysis of Java program concepts for visualization and profiling

Concept assignment identifies units of source code that are functionally related, even if this is not apparent from a syntactic point of view. Until now, the results of concept assignment have only been used for static analysis, mostly of program source code. This paper investigates the possibility of using concept information within a framework for dynamic analysis of programs. The paper presents two case studies involving a small Java program used in a previous research exercise, and a large Java virtual machine (the popular Jikes RVM system). These studies investigate two applications of dynamic concept information: visualization and profiling. The paper demonstrates two different styles of concept visualization, which show the proportion of overall time spent in each concept and the sequence of concept execution, respectively. The profiling study concerns the interaction between runtime compilation and garbage collection in Jikes RVM. For some benchmark cases, we are able to obtain a significant reduction in garbage collection time. We discuss how this phenomenon might be harnessed to optimize the scheduling of garbage collection in Jikes RVM.