基于双边多议题的Pareto最优协商研究

研究完全信息情况下的双边多议题协商问题,提出一种双方反提议决策模型,在每一轮反提议生成过程中,提议方在满足自身效用在该轮的保留效用水平下,最大化对方的效用,从而使协商结果达到Pareto最优,同时对双方在不同让步策略下的协商结果进行算例分析,为基于双方合作的完全信息协商提供理论参考。     

Many-to-Many Communication in Radio Networks

Radio networks model wireless data communication when the bandwidth is limited to one wave frequency. The key restriction of such networks is mutual interference of packets arriving simultaneously at a node. The many-to-many (m2m) communication primitive involves p participant nodes from among n nodes in the network, where the distance between any pair of participants is at most d. The task is to have all the participants get to know all the input messages. We consider three cases of the m2m communication problem. In the ad-hoc case, each participant knows only its name and the values of n, p and d. In the partially centralized case, each participant knows the topology of the network and the values of p and d, but does not know the names of the other participants. In the centralized case, each participant knows the topology of the network and the names of all the participants. For the centralized m2m problem, we give deterministic protocols, for both undirected and directed networks, working in time, which is provably optimal. For the partially centralized m2m problem, we give a randomized protocol for undirected networks working in time with high probability (whp), and we show that any deterministic protocol requires time. For the ad-hoc m2m problem, we develop a randomized protocol for undirected networks that works in time whp. We show two lower bounds for the ad-hoc m2m problem. One lower bound states that any randomized protocol for the m2m ad hoc problem requires expected time. Another lower bound states that for any deterministic protocol for the m2m ad hoc problem, there is a network on which the protocol requires time when n−p(n)=Ω(n) and d>1, and that it requires Ω(n) time when n−p(n)=o(n). The results of this paper appeared in a preliminary form in “On many-to-many communication in packet radio networks” in Proceedings of the 10th Conference on Principles of Distributed Systems (OPODIS), Bordeaux, France, 2006, Lecture Notes in Computer Science 4305, Springer, Heidelberg, pp. 258–272. The work of B.S. Chlebus was supported by NSF Grant 0310503.     

传感器网络中基于帕累托最优效用的包转发研究

本文运用博弈论的观点和方法来解决传感器网络中的包转发问题。为传感器网络建立了包转发模型,分析了节点参与包转发会话所获得的帕累托最优效用,提出了基于帕累托最优效用的包转发算法POUPF,并证明了该算法能够建立纳什均衡以保证每个节点都获得帕累托最优效用。仿真结果表明：POUPF能够有效促进节点自发合作,确保了每个节点获得帕累托最优效用;任何偏离POUPF节点的包转发行为都会导致所有节点效用的下降。     

基于Pareto最优的QoS路由算法

QoS路由是QoS框架中的重要组成部分,旨在寻找多约束条件下的可行路径.在解决多约束(MCP)问题时,引入了Pareto最优概念.基于此概念,提出了基于Pareto最优的QoS权重空间划分模型.在该模型中,根据路由请求与MCP问题解的关系,很容易判定路由请求是否能够被满足.在模型基础上,提出了用于解决具有两可加约束的动态权重系数路由算法PODWCA,它平均只需要运行2～3次,Dijkstra算法就能达到很高的性能.仿真结果验证了PODWCA算法的有效性.     

多目标优化问题的有效Pareto最优集

多目标优化问题求解是当前演化计算的一个重要研究方向,而基于Pareto最优概念的遗传算法更是研究的重点,然而,遗传算法在解决多目标优化问题上的缺陷却使得其往往得不到一个令人满意的解。在对该类算法研究的基础上提出了衡量Pareto最优解集的标准,并对如何满足这个标准提出了建议。     

Pareto Optimal Decentralized Control of a Rotor in Electromagnetic Bearings

Journal of Computer and Systems Sciences International - The problem of the optimal stabilization of a vertical rigid rotor rotating in two electromagnetic bearings is considered. A quantitative...     

基于遗传多目标优化算法的Pareto最优解研究

遗传算法可有效求解多目标优化问题中的Pareto最优解,并利用MATLAB进行了仿真验证。     

Three-Sided Stable Matchings with Cyclic Preferences

Knuth (Mariages Stables, Les Presses de L’Université de Montréal, 1976) asked whether the stable matching problem can be generalised to three dimensions, e.g., for families containing a man, a woman and a dog. Subsequently, several authors considered the three-sided stable matching problem with cyclic preferences, where men care only about women, women only about dogs, and dogs only about men. In this paper we prove that if the preference lists may be incomplete, then the problem of deciding whether a stable matching exists, given an instance of the three-sided stable matching problem with cyclic preferences, is NP-complete. Considering an alternative stability criterion, strong stability, we show that the problem is NP-complete even for complete lists. These problems can be regarded as special types of stable exchange problems, therefore these results have relevance in some real applications, such as kidney exchange programs.     

基于Pareto最优解的零件制作方向优化研究

分析了快速成型工艺中零件制作方向对制件表面质量、所需支撑面积和零件制造时间的影响,分别建立了它们的优化数学模型。采用了基于Pareto最优解的多目标优化遗传算法NSGA－II进行优化计算,通过与单目标优化方法求得最优结果的对比,表明用多目标优化方法进行零件制作方向的优化计算,不仅可以求出比单目标方法更优的解,而且通过一次优化计算就可得到多个较优的零件制作方向。     

基于Pareto最佳解的P3P隐私协商机制研究

提出并实现了基于Pareto最佳解实现两方协商过程中自动达成协商结果的算法,结合实验数据说明了算法相对于协商双方的公正性,并分析了参数选择对协商结果的影响。最后结合实例提出将该算法应用于P3P隐私协商的可能性,并给出了算法的改进方向。     

高效求解Pareto最优前沿的多目标进化算法

设计了一种新的求解均匀分布的Pareto最优解集的多目标进化算法(MOEA),其主要的特点是使用了一种新的个体适应值的计算方式,方法是通过群体中某一个体与群体的最优非劣解集的最小距离来刻画个体的适应值的.算法还结合了遗传算法中的精英策略以及NSGA-Ⅱ中的拥挤距离[12],提高了非劣解向Pareto最优前沿收敛的速度,并且保证了Pareto 最优解集的多样性.仿真结果表明,算法不仅能够获得分布良好的Pareto最优前沿,而且能够极大地简化计算,减少了算法的运行时间,其计算复杂度为o(mn2)(m表示的是目标函数的个数,n是种群的规模).     

用擂台赛法则构造多目标Pareto最优解集的方法

针对多目标进化的特点,提出了用擂台赛法则(arena's principle,简称AP)构造多目标Pareto最优解集的方法,论证了构造方法的正确性,分析了其时间复杂度为O(rmN)(0＜m/N＜1).理论上,当AP与Deb的算法以及Jensen的算法比较时(它们的时间复杂度分别为O(rN²)和O(Nlog^(r-1)N)),AP优于Deb的算法;当目标数r较大时(如r≥5),AP优于Jensen的算法;此外,当m/N较小时(如m/N≤50%),AP的效率与其他两种算法比较具有优势.对比实验结果表明,AP具有比其他两种算法更好的CPU时间效率.在应用中,AP可以被集成到任何基于Pareto的MOEA中,并能在较大程度上提高MOEA的运行效率.     

一种基于节点能力区分的Pareto最优网络编码组播方法

快速数据分发在突发事件响应,军事领域等具有重要的应用。针对异构用户节点群体下快速数据分发问题,提出基于能力区分的拓扑构建和速率控制的网络编码组播协议CORE。CORE利用能力区分的自适应层次化拓扑构建鼓励节点提供高的上传带宽并优化系统范围吞吐率;利用直方图的方式对基于网络编码的数据传输进行流量控制,降低冗余数据的传输;基于分布式的速率控制实现Pareto最优的下载速率分配。实验结果表明CORE具有良好的可扩展性,能够充分利用异构节点的上传能力,提供区分的下载带宽分配,较高的数据传输吞吐率、低端到端网络延迟,能够提供异构网络环境下分发时间紧迫的数据分发服务。     

双目标有功经济负荷分配模型及算法研究

建立了以全网煤耗最小,成本节约最大为双目标的电力系统有功经济负荷分配模型,将模糊 经理论与线性规划方法有机结合提出了求解双目标、大规模、非线性经济负荷分配型的有效方法。仿真算例的结果验证了本语文模型和算法的正确性和有效性。     

求解多目标流水车间调度Pareto最优解的遗传强化算法

针对多目标流水车间调度Pareto最优问题, 本文建立了以最大完工时间和最大拖延时间为优化目标的多目标流水车间调度问题模型, 并设计了一种基于Q-learning的遗传强化学习算法求解该问题的Pareto最优解. 该算法引入状态变量和动作变量, 通过Q-learning算法获得初始种群, 以提高初始解质量. 在算法进化过程中, 利用Q表指导变异操作, 扩大局部搜索范围. 采用Pareto快速非支配排序以及拥挤度计算提高解的质量以及多样性, 逐步获得Pareto最优解. 通过与遗传算法、NSGA-II算法和Q-learning算法进行对比实验, 验证了改进后的遗传强化算法在求解多目标流水车间调度问题Pareto最优解的有效性.     

Optimal Tax Progressivity in Unionised Labour Markets: Simulation Results for Germany

Changing the income tax progressivity in labour markets with collective wage bargaining generates a trade-off. On the one hand, higher progressivity distorts individual labour supply decisions at the hours-of-work margin, on the other hand, it reduces unemployment by exerting downward pressure on wages. This trade-off is quantitatively assessed using a numerical model for Germany. The model combines a microsimulation module, which captures the labour-supply decisions of approximately 4,600 individual households, and a macro (computable general equilibrium) module, which features collective wage bargaining and involuntary unemployment. In the simulations carried out using this model, the optimal degree of tax progressivity turns out to be higher than the one in the actual German tax schedule. The optimum is located at marginal tax rates that are 6 percentage points higher than the actual rates (combined with a transfer that balances the public budget). The welfare gain from such a reform is modest, however. It amounts to no more than two euros per person per month.     

社会网络中弱关系团队形成问题研究

随着在线社会网络的迅速发展,社会网络的团队形成问题逐渐成为研究热点。现有的社会网络中团队形成问题目标是寻找一个成员间沟通代价最小的团队。然而,实际应用中团队成员间的不紧密关系使得团队的观点多样化、多角度、无偏见,可以广泛应用于形成专家评审团队、大众评审团等。基于此需求,将社会学的弱关系概念引入团队形成问题中,提出了一种社会网络中弱关系团队形成问题。该问题旨在寻找成员间为弱关系,同时满足技能、经验值要求的一个团队,为NP-hard问题。提出了3类算法解决该问题,分别为贪心算法、精确算法、α-近似算法,每类算法有各自的特点与适用范围。利用ACM和DBLP两类真实的数据集进行实验,综合评估了各类算法的效率与求解质量,证明了提出算法的有效性。     

Log-Space Algorithms for Paths and Matchings in k-Trees

Reachability and shortest path problems are NL-complete for general graphs. They are known to be in L for graphs of tree-width 2 (Jakoby and Tantau in Proceedings of FSTTCS’07: The 27th Annual Conference on Foundations of Software Technology and Theoretical Computer Science, pp. 216–227, 2007). In this paper, we improve these bounds for k-trees, where k is a constant. In particular, the main results of our paper are log-space algorithms for reachability in directed k-trees, and for computation of shortest and longest paths in directed acyclic k-trees. Besides the path problems mentioned above, we also consider the problem of deciding whether a k-tree has a perfect matching (decision version), and if so, finding a perfect matching (search version), and prove that these two problems are L-complete. These problems are known to be in P and in RNC for general graphs, and in SPL for planar bipartite graphs, as shown in Datta et al. (Theory Comput. Syst. 47:737–757, 2010). Our results settle the complexity of these problems for the class of k-trees. The results are also applicable for bounded tree-width graphs, when a tree-decomposition is given as input. The technique central to our algorithms is a careful implementation of the divide-and-conquer approach in log-space, along with some ideas from Jakoby and Tantau (Proceedings of FSTTCS’07: The 27th Annual Conference on Foundations of Software Technology and Theoretical Computer Science, pp. 216–227, 2007) and Limaye et al. (Theory Comput. Syst. 46(3):499–522, 2010).