SUBJECTIVE RADE-OFF RATE METHOD OF MULTIOBJECTIVE DECISION-MAKING

ＳＵＢＪＥＣＴＩＶＥＲＡＤＥ－ＯＦＦＲＡＴＥＭＥＴＨＯＤＯＦＭＵＬＴＩＯＢＪＥＣＴＩＶＥＤＥＣＩＳＩＯＮ－ＭＡＫＩＮＧ￥ＷａｎｇＸｉａｎｊｉａ（王先甲）（ＷｕｈａｎＵｎｉｖｅｒｓｉｔｙｏｆＨｙｄｒａｕｌｉｃａｎｄＥｌｅｃｔｒｉｃＥｎｇｉｎｅｅｒｉｎｇ...     

EVOLUTIONARY DYNAMICS ON ONE-DIMENSIONAL CYCLE WITH SHIFTING MECHANISM AND TINY MUTATION RATE

In this paper we study the impact of tiny mutation on the evolutionary dynamics on one-dimensional cycle with shifting mechanism. The evolutionary success is evaluated by investigating the stationary distribution of the ergodic process with the idea of viscosity solutions. The cooperative behaviors in ecosystem and social system are briefly discussed by applying the results to the prisoner’s dilemma game.     

基于全牛顿求解P*(κ)阵水平线性互补问题的内点算法

提出一种求解P_*(k)阵水平线性互补问题的全牛顿内点算法,全牛顿算法的优势在于每次迭代中不需要线性搜寻.当给定适当的中心路径邻域的阈值和更新势垒参数,证明算法中心邻域的全牛顿是局部二次收敛的,最后给出算法迭代复杂性O(√n)log(n+1+k)/εμ₀.     

进化博弈决策机制设计综述

进化博弈论是一门交叉性强的综合性理论,在国内外已得到广泛研究和应用.本文系统论述了进化博弈的决策机制及其特点,指出了进化博弈研究的趋势,进化博弈论可作为中国科技工作者学习、研究和应用的有力工具.     

基于AHP和动量BP神经网络的工程项目承包商选择模型

运用BP神经网络技术,采用动量BP算法,构建了基于动量BP神经网络的工程项目承包商选择模型,并将AHP的评价结果作为学习样本,对BP神经网络模型进行训练和测试.结果表明,基于AHP和动量BP神经网络的工程项目承包商选择模型是可行的,该模型具有较高的自组织、自适应和自学习能力以及较强的容错功能,能够为一般的工程项目承包商选择活动提供有效的参考和依据.     

二(双)层规划综述

二(双)层规划是研究二层决策的递阶优化问题.其理论、方法和应用在过去的30多年取得了很大的发展.本文对二层规划问题的基本概念、性质和算法作了综述,并且对下层规划问题的解不唯一的情况也作了介绍,最后还给出了几种常见的二层规划模型.     

DISRUPTION MANAGEMENT FOR SUPPLY CHAIN COORDINATION WITH EXPONENTIAL DEMAND FUNCTION

The coordination problem of a supply chain comprising one supplier and one retailer under market demand disruption is studied in this article. A novel exponential demand function is adopted, and the penalty cost is introduced explicitly to capture the deviation production cost caused by the market demand disruption. The optimal strategies are obtained for different disruption scale under the centralized mode. For the decentralized mode, it is proved that the supply chain can be fully coordinated by adjusting the price discount policy appropriately when disruption occurs. Furthermore, the authors point out that similar results can be established for more general demand functions that represent different market circumstances if certain assumptions are satisfied.     

具有模糊支付的Moran过程演化博弈动态

以往对演化博弈的研究都假设个体从博弈中获得的支付是确定的并以精确的数来表示。然而由于受环境中各种不确定因素的影响,个体博弈时所获得的支付并不是一个精确的数值,而需要用一个模糊数来表示。本文研究模糊支付下2×2的对称博弈, 利用模糊数的运算, 分析具有模糊支付的有限种群Moran过程演化动态。在弱选择下以梯形模糊数和三角模糊数表示博弈支付,计算策略的模糊扎根概率,分析自然选择有利于策略扎根及策略成为模糊演化稳定策略的条件。将经典博弈推广到模糊环境中丰富了演化博弈理论,更具有现实意义。     

Stochastic evolutionary public goods game with first and second order costly punishments in finite populations

We study the stochastic evolutionary public goods game with punishment in a finite size population. Two kinds of costly punishments are considered, i.e., first-order punishment in which only the defectors are punished, and second-order punishment in which both the defectors and the cooperators who do not punish the defective behaviors are punished. We focus on the stochastic stable equilibrium of the system. In the population, the evolutionary process of strategies is described as a finite state Markov process. The evolutionary equilibrium of the system and its stochastic stability are analyzed by the limit distribution of the Markov process. By numerical experiments, our findings are as follows.(i) The first-order costly punishment can change the evolutionary dynamics and equilibrium of the public goods game, and it can promote cooperation only when both the intensity of punishment and the return on investment parameters are large enough.(ii)Under the first-order punishment, the further imposition of the second-order punishment cannot change the evolutionary dynamics of the system dramatically, but can only change the probability of the system to select the equilibrium points in the "C+P" states, which refer to the co-existence states of cooperation and punishment. The second-order punishment has limited roles in promoting cooperation, except for some critical combinations of parameters.(iii) When the system chooses"C+P" states with probability one, the increase of the punishment probability under second-order punishment will further increase the proportion of the "P" strategy in the "C+P" states.     

基于神经网络与群智能的多代理人决策模型及其应用

本文提出了一种基于神经网络与群智能技术的多代理人决策模型.该决策模型以神经网络作为决策控制器,神经网络的输入层是代理人的历史行为策略,输出层决定了代理人的当前策略,神经网络的权重通过群智能优化技术进行训练.权重值的更新过程刻画了代理人行为策略的动态变化过程.仿真实验表明该决策模型具有自适应学习的能力,并能克服代理人之间的冲突取得Pareto最优.