网格资源分配的进化博弈策略

针对网格资源分配中的竞争问题,提出了一种利用进化博弈的动态机制研究资源分配的方法。该方法利用复制动态方程求解网格使用者策略选择比例的进化稳定点,通过反复博弈使得网格使用者学习并调整出价策略,并讨论了四种典型的使用者评估函数对进化稳定点的影响。最后利用网格模拟器进行了实验评估,结果表明提出的进化博弈方法是收敛的,且在网格使用者的总体效用方面优于传统算法,从而实现了网格资源的优化分配。     

基于进化博弈的网格资源分配方法的研究

在网络资源优化分配问胚的研究中,由于用户存在决策失误,现有基于理性用户博弈的网格资源分配在实际网格环境会完全失效.分析了非完全理性网格用户群体的资源分配策略及其演化过程,提出一种改进的复制动态机制的网格资源分配方法,克服了用户理性的限制,引入变异机制,在无初始学习样本的情况下,也能确保用户达到进行稳定策略点,实现了网格资源在有限理性用户之间的优化分配.仿真表明,用户通过学习对资源分配策略进行调整,可实现最优分配策略并处于稳定状态,证明了进化博弈的资源分配方法在网格环境中的适应性和稳定性.     

云计算基于随机进化博弈动态的资源优化配置

云资源优化分配研究中,用户策略难免存在失误,以用户完全理性为前提的资源分配方法在实际环境下会出现失效。分析非完全理性的云用户种群下的资源分配过程及其策略演化过程,提出一种随机动态模型求解有限云种群中的资源分配进化博弈问题。模型克服用户完全理性的限制,利用带有频率依赖选择的Moran过程,在重复博弈过程中寻找云用户策略的入侵指数和固定指数策略选择偏好的条件。根据经济学云环境的特征,建立云用户的固定效用矩阵,量化策略选择动态。数值仿真结果证明,为了最大化自身效用,不同大小云种群中的个体策略会向着不同策略方向演化,资源在有限理性用户之间可以实现优化分配,且最优分配策略最终会处于稳定状态。     

基于博弈理论的经济网格资源配置研究

针对经济网格中,由于网格系统的复杂性和用户的私利性,使得网格用户在资源竞价过程中往往因相关信息的匮乏而导致资源竞价的盲目性问题,根据重复博弈分阶段执行的特点,将网格用户间对网格资源的竞争看作多阶段的重复博弈过程.用户依据前一阶段博弈的竞价值及竞价结果对当前阶段的竞价策略进行调整,通过有限次的阶段博弈达到均衡出价策略组合,实现用户最大效用下的资源分配.仿真表明,在不完全信息的网格环境中,该竞价模型可逐步改善网格用户的资源竞价策略,实现优化目标最大化下的网格资源分配.     

有限理性下的企业合作竞争的进化博弈分析

在有限理性的条件下,应用进化博弈论中的模仿者动态模型来分析企业合作竞争博弈的演化,用进化稳定策略来描述合作竞争博弈的长期演化趋势。并分别讨论了对称和非对称的合作竞争博弈的进化均衡与稳定性。最后,指出确定性模仿者动态模型的不足,以及当今随机动态系统理论的发展。     

一种基于序贯博弈的网格资源分配策略

网格环境中资源的负载预测是实现资源优化分配的关键任务之一,而网格资源的动态性和异构性使得准确判断资源的负载状态十分困难.针对已有的分配策略对资源负载评估的不足,提出了一种基于序贯博弈的优化用户时间的网格资源分配策略.该策略将正比例资源共享的网格环境中多用户竞争同一计算资源的问题形式化为一个多人序贯博弈,通过寻求该序贯博弈中各个阶段博弈的纳什均衡解来预测资源负载;然后利用此负载信息生成所有用户的最优出价组合和资源的优化价格;最后根据各用户出价,按比例分配资源的计算能力.通过对网格模拟器GridSim的实验研究,结果表明,该策略能够得到合理的用户出价,降低资源占用时间,从而弥补了Bredin提出的优化策略中未考虑资源未来负载变化的缺陷,实现了资源的优化分配.其结论说明运用序贯博弈方法预测资源负载是可行的,且能更好地适应网格环境下异构资源的动态性.     

基于非合作博弈效用最优化的云资源提供策略

为解决云计算资源提供过程中用户的异构性需求问题,提出一种基于非合作博弈效用最优化的云资源提供策略.利用比例共享机制,根据用户的出价提供资源,对用户的出价函数进行求解,并证明效用最优化模型存在非合作博弈纳什均衡解.实验结果表明,该策略能够反映用户需求与资源价格之间的浮动关系,规范用户的出价与资源分配,在公平性、均衡性和合理性上均有较好的效果.     

协作式云资源博弈分配

针对用户对云资源的异构性需求和竞争问题,提出了一种协作式资源分配博弈策略。建立了资源分配的协作式博弈模型,定义了协作博弈的用户效用函数和评估函数,证明了在该效用函数下协作博弈存在唯一Nash均衡,并讨论了用户组建联盟对协作特征函数和整体效用的影响。实验结果表明,在该协作博弈策略下,个体用户通过组建联盟集体出价的方式,能够为联盟用户带来更大效用,以收敛方式实现Pareto改进。     

一种基于效用函数的网格资源分配策略

针对网格资源分配中用户需求的异构性问题，提出了一种基于效用函数优化的分配策略。该策略综合考虑用户作业执行费用和执行时间两方面的因素，利用拉格朗日方法解决网格用户效用函数的优化问题，通过二分搜索最优解产生一组优化的用户出价，根据该组出价按比例划分资源的计算能力。该分配策略可对网格资源的价格以及资源的占用时间进行优化，对动态、异构的网格环境具有较好的适　　应性。     

动态可靠性约束的多阶段测试资源分配研究

为满足测试资源分配过程中用户对软件可靠性的需求,构建一种动态可靠性约束的多阶段多目标测试资源分配模型DRC-MSMOTRA。从理论上分析不同阶段满足可靠性约束的测试时间下限并设计相应的种群初始化策略,结合参数估计、加权归一化方法和多目标差分进化,提出一种动态可靠性约束的多阶段多目标测试资源分配算法MS-DRC-GDE3。实验结果表明,与MSMOTRA模型相比,DRC-MSMOTRA模型在2种不同规模的软件系统上所获解的覆盖值分别提高约62和59个百分点,与MS-GDE3算法相比,MS-DRC-GDE3算法在2种软件系统上所获解的覆盖值分别提高约69和80个百分点,即所提模型和算法能够根据用户对可靠性的需求来为用户提供更多更优的测试资源分配方案。     

复杂网络上的演化博弈

主要介绍了近年来复杂网络上的演化博弈研究现状和研究方向．复杂网络理论的发展为描述博弈关系提供了系统且方便的框架，网络上的节点表示博弈个体，边代表与其邻居的博弈关系．介绍了经典演化博弈论中的演化稳定策略概念和复制动力学方程，以及二者的相互联系．介绍了混合均匀有限人口中随机演化动力学问题，并给出了与确定复制方程的相互转化关系．介绍了小世界、无标度等复杂网络上演化博弈的研究结论，给出了复杂网络上演化博弈论的未来发展方向．     

异构无线网络用户网络关联优化:一种基于群体博弈的方法

针对异构无线网络(Heterogeneous Wireless Networks,HWNs)负载平衡问题,提出了一种基于群体博弈的用户网络关联方案.首先将HWNs系统用户网络关联问题抽象成一个群体博弈模型,根据用户在网络中得到的收益函数,证明该群体博弈满足势博弈的条件.利用复制动态作为演化动态工具,证明演化的结果最终会收敛到纳什均衡,这个特性确保了每个用户关联到一个效用最优的网络.然后证明纳什均衡点能最大化整个HWNs系统的吞吐量,保证了纳什均衡的有效性.最后,基于复制动态原理提出了用户网络关联算法.仿真实验模拟了用户网络选择过程,得到了均衡点,验证了理论分析的结果.     

无线移动网络节点发包概率的演化博弈模型

针对大规模无线移动网络中节点对信道资源的群体性随机博弈问题,在载波侦听多路访问/冲突避免(CSMA/CA)的机制下,建立一种节点发包概率的演化博弈模型。考虑节点成功发送数据包的收益、监听、退避和冲突等成本因素,给出并证明该模型的演化稳定策略(ESS),推导节点发包概率演化的复制动态方程。数值仿真结果表明,该模型能提供更强的ESS,保证在多个移动节点干扰时演化稳定点的鲁棒性。     

基于进化博弈的P2P网络中信任计算的动力学分析*

运用动力学原理,基于进化博弈理论,对信任计算的动力学方程进行了求解分析,并运用复制动态原理分析了节点之间信任关系的演化趋势,进一步揭示了信任计算的演化动力学规律。仿真实验表明,进化是网络节点信任合作的动力源泉。     

Graph-based quadratic optimization: A fast evolutionary approach

Quadratic optimization lies at the very heart of many structural pattern recognition and computer vision problems, such as graph matching, object recognition, image segmentation, etc., and it is therefore of crucial importance to devise algorithmic solutions that are both efficient and effective. As it turns out, a large class of quadratic optimization problems can be formulated in terms of so-called “standard quadratic programs” (StQPs), which ask for finding the extrema of a quadratic polynomial over the standard simplex. Computationally, the standard approach for attacking this class of problems is to use replicator dynamics, a well-known family of algorithms from evolutionary game theory inspired by Darwinian selection processes. Despite their effectiveness in finding good solutions in a variety of applications, however, replicator dynamics suffer from being computationally expensive, as they require a number of operations per step which grows quadratically with the dimensionality of the problem being solved. In order to avoid this drawback, in this paper we propose a new population game dynamics (InImDyn) which is motivated by the analogy with infection and immunization processes within a population of “players.” We prove that the evolution of our dynamics is governed by a quadratic Lyapunov function, representing the average population payoff, which strictly increases along non-constant trajectories and that local solutions of StQPs are asymptotically stable (i.e., attractive) points. Each step of InImDyn is shown to have a linear time/space complexity, thereby allowing us to use it as a more efficient alternative to standard approaches for solving StQPs and related optimization problems. Indeed, we demonstrate experimentally that InImDyn is orders of magnitude faster than, and as accurate as, replicator dynamics on various applications ranging from tree matching to image registration, matching and segmentation.     

Replicator dynamics under perturbations and time delays

Along with the appearance of new optimization and control problems, novel paradigms emerge. A large number of them are based on behavioral ecology, where population dynamics play an important role. One of the most known models of population dynamics is the replicator equation, whose applications in optimization and control have increased in recent years. This fact motivates the study of the replicator dynamics’ properties that are related to the implementation of this method for solving optimization and control problems. This paper addresses implementation issues of the replicator equation in engineering problems. We show by means of the Lyapunov theory that the replicator dynamics model is robust under perturbations that make the state to leave the simplex (among other reasons, this phenomenon can emerge due to numerical errors of the solver employed to obtain the replicator dynamic’s response). A refinement of these results is obtained by introducing a novel robust dynamical system inspired by the replicator equation that allows to control and optimize plants under arbitrary initial conditions on the positive orthant. Finally, we characterize stability bounds of the replicator dynamics model in problems that involve N strategies that are subject to time delays. We illustrate our results via simulations.     

A Class of Evolutionary Models for Participation Games with Negative Feedback

We introduce a framework to analyze the interaction of boundedly rational heterogeneous agents repeatedly playing a participation game with negative feedback. We assume that agents use different behavioral rules prescribing how to play the game conditionally on the outcome of previous rounds. We update the fraction of the population using each rule by means of a general class of evolutionary dynamics based on imitation, which contains both replicator and logit dynamics. Our model is analyzed by a combination of formal analysis and numerical simulations and is able to replicate results from the experimental and computational literature on these types of games. In particular, irrespective of the specific evolutionary dynamics and of the exact behavioral rules used, the dynamics of the aggregate participation rate is consistent with the symmetric mixed strategy Nash equilibrium, whereas individual behavior clearly departs from it. Moreover, as the number of players or speed of adjustment increase the evolutionary dynamics typically becomes unstable and leads to endogenous fluctuations around the steady state. These fluctuations are robust with respect to behavioral rules that try to exploit them.     

Replicator Dynamics of Evolutionary Hypergames

In evolutionary game theory, the distribution of strategies in the population is changed according to payoffs which individuals earn depending on their selected strategies. However, to the best of our knowledge, individuals' perceptions in evolutionary games have been disregarded. Individuals that select a strategy by trial and error may perceive the same conflict situation from different viewpoints. Hence, the influence of individuals' perceptions has to be taken into consideration. In this correspondence, interpretation functions in hypergames are introduced into evolutionary games. Using them, we formulate replicator dynamics considering individuals' perceptions. In a special case that there exist two populations, we show that its interior equilibrium point is stable if the corresponding point is a hyper-Nash-Pareto pair     

可求解多目标优化问题的演化博弈优化算法

借鉴演化博弈的思想和选择机制,提出了一种新的基于演化博弈的优化算法(EGOA)用于多目标问题的求解.算法框架具备对该类问题的通用性.为了对算法性能进行评估,采用了一组多目标优化问题(MOPs)的测试函数进行实验.实验结果表明,使用本算法搜索得到的演化稳定策略集合能够很好地逼近多目标优化问题的帕累托前沿,与一些经典的演化算法相比具有良好的问题求解能力.