MULTI-OBJECTIVE SHAPE DESIGN IN AERODYNAMICS USING GAME STRATEGY

Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.

Multi-objective optimization strategies using adjoint method and game theory in aerodynamics

There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

The Coalition Cooperative Game Method and Its Application in Multi-objective Optimization Design

This paper proposes a multi-objective optimization design method based on the coalition cooperative game theory where the three design goals have been seen as three game players. By calculating the affecting factors and fuzzy clustering, the design variables are divided into different strategic spaces which belong to each player, then it constructs a payoff function based on the coalition mechanism. Each game player takes its own revenue function as a target and obtains the best strategy versus other players. The best strategies of all players consist of the strategy permutation of a round game and it obtains the final game solutions through multi-round games according to the convergence criterion. A multi-objective optimization example of the luff mechanism of compensative sheave block shows the effectiveness of the coalition cooperative game method.

An Analytic Method for Interval Bimatrix Games

This paper deals with rnxn two-person non-zero sum games with interval pay-offs. An analytic method for solving such games is given. A pair of Nash Equilibrium is found by using the method. The analytic method is effective to find at least one Nash Equilibrium （N.E） for two-person bimatrix games. Therefore, the analytic method for two-person bimatrix games is adapted to interval bimatrix games.

Multi-objective optimization for voltage and frequency control of smart grids based on controllable loads

The output uncertainty of high-proportion distributed power generation severely affects the system voltage and frequency.Simultaneously,controllable loads have also annually increased,which markedly improve the capability for nodal-power control.To maintain the system frequency and voltage magnitude around rated values,a new multi-objective optimization model for both voltage and frequency control is proposed.Moreover,a great similarity between the multiobjective optimization and game problems appears.To reduce the strong subjectivity of the traditional methods,the idea and method of the game theory are introduced into the solution.According to the present situational data and analysis of the voltage and frequency sensitivities to nodal-power variations,the design variables involved in the voltage and frequency control are classified into two strategy spaces for players using hierarchical clustering.Finally,the effectiveness and rationality of the proposed control are verified in MATLAB.

Network Security Transmission Based on Bimatrix Game Theory

Based on the bimatrix game theory, the network data transmission has been depicted in a game theory way： the actions of the attacker and defender （legitimate users） are depicted within a two-person, non-cooperative and bimatrix game model, this paper proves the existence of the Nash equilibrium theoretically, which is further illustrated by the experimental resuhs.

Some Kinds of Bargaining Equilibria of Multi-objective Games

To solve the choice of multi-objective game's equilibria,we construct general bargaining games called self-bargaining games,and define their individual welfare functions with three appropriate axioms.According to the individual welfare functions,we transform the multi–objective game into a single-objective game and define its bargaining equilibrium,which is a Nash equilibrium of the single-objective game.And then,based on certain continuity and concavity of the multi-objective game's payoff function,we proof the bargaining equilibrium still exists and is also a weakly Pareto-Nash equilibrium.Moreover,we analyze several special bargaining equilibria,and compare them in a few examples.

Piecewise linear programming approach to solve multi-objective matrix games with I-fuzzy goals

The intuitionistic fuzzy set(I-fuzzy set)plays an effective role in game theory when players face‘neither this nor that’situation to set their goals.This study presents a maxmin–minmax solution to multi-objective two person zero-sum matrix games with I-fuzzy goals.In this article,a class of piecewise linear membership and non-membership functions for I-fuzzy goals is constructed.These functions are more effective in real games because marginal rate of increase(decrease)of such membership functions(non-membership functions)is different in different intervals of tolerance errors.Finally,one numerical example is given to examine the effectiveness and advantages of the proposed results.

Game Theory Based Network Security

The interactions between attackers and network administrator are modeled as a non-cooperative non-zero-sum dynamic game with incomplete information, which considers the uncertainty and the special properties of multi-stage attacks. The model is a Fictitious Play approach along a special game tree when the attacker is the leader and the administrator is the follower. Multi-objective optimization methodology is used to predict the attacker’s best actions at each decision node. The administrator also keeps tracking the attacker’s actions and updates his knowledge on the attacker’s behavior and objectives after each detected attack, and uses it to update the prediction of the attacker’s future actions. Instead of searching the entire game tree, appropriate time horizons are dynamically determined to reduce the size of the game tree, leading to a new, fast, adaptive learning algorithm. Numerical experiments show that our algorithm has a significant reduction in the damage of the network and it is also more efficient than other existing algorithms.

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.

基于双矩阵博弈的群组聚合模型

研究了多个独立个体组成的群组的聚合问题.首先,将个体间的交互作用建模为双矩阵博弈.其次,研究了完全信息个体和不完全信息个体组成的群组的聚合问题,给出了群组完成聚合的充分条件,并进一步证明了最终聚合状态在初始状态所形成的凸包内.最后,用几个仿真算例验证了理论结果的正确性,并分析了聚合时间的影响因素.

多重纳什均衡解的粒子群优化算法

提出了一种求解双矩阵对策多重纳什均衡解的粒子群优化算法。该算法通过随机初始点以及迭代粒子的归一化,保证粒子群始终保持在对策的可行策略空间内,避免了在随机搜索中产生无效的粒子,提高了粒子群优化算法求解纳什均衡解的计算性能。最后给出了几个数值例子,说明了粒子群优化算法的高效性。

一类模糊多目标双矩阵对策的粒子群优化算法

针对一类具有模糊目标的多目标双矩阵对策给出了基于粒子群优化的求解算法。讨论了当模糊目标的隶属函数是线性函数时纳什均衡解的判定定理;构造的粒子群优化算法,通过随机初始点以及迭代粒子的归一化,保证粒子群始终保持在对策的可行策略空间内,避免了在随机搜索中产生无效的粒子,提高了用粒子群优化算法求解纳什均衡解的计算性能。给出的一个数值算例验证了该算法的有效性。

具有策略偏好的模糊双矩阵对策模型及其应用

针对局中人对策略有偏好、支付值模糊的双矩阵对策给出了一种具体的求解方法,首先根据加权算子集结得到局中人的策略偏好向量、利用模糊数排序方式将模型转化为清晰的双矩阵对策,再利用粒子群优化算法求解。最后以雷达电子对抗为例,建立了具有策略偏好的模糊双矩阵对策的作战效能评估模型,研究结果对于双方资源分配,提高作战效能具有一定的军事应用价值。

求双矩阵对策解的方法

双矩阵对策问题是一个尚未得到较一般求解方法的困难问题.该文给出了一个求双矩阵对策的解的一般方法.

基于粒子群优化的混合模糊双矩阵对策求解

针对具有模糊策略集与模糊支付值的不确定性冲突环境,建立了混合模糊双矩阵对策模型。在假定模糊支付值为三角模糊数的情形下,采用了基于单个截集的模糊数线性排序函数,将模型清晰化后转化为双矩阵对策,并应用粒子群优化算法求解。最后,给出一个军事例子说明了模型的实用有效性和粒子群优化算法的高效性。

多冲突环境下混合模糊双矩阵对策的集结

针对策略集模糊与具有模糊支付值的多冲突环境,建立了混合模糊双矩阵对策的综合集结模型。基于局中人的约束条件,构造局中人面向多冲突环境的可行策略串集合,建立新的结局空间;在假定模糊支付值为三角模糊数的情形下,基于模糊数的运算,构造局中人在新结局空间上的合成结局模糊支付值,建立多冲突环境下的混合模糊双矩阵对策综合集结模型,将集结模型清晰化并求解。最后,给出军事想定实例,说明了模型的实用性及有效性。

基于模糊双矩阵对策的作战效能评估模型

讨论了策略集清晰、支付值模糊的模糊双矩阵对策的一种求解方法，并以某型导弹混编群对抗ARM（反辐射导弹）及载机为案例进行研究，建立了基于模糊双矩阵对策的作战效能评估模型，研究结果对于双方资源分配，提高作战效能具有一定的军事应用价值。

两人多冲突环境下的双矩阵对策集结模型

针对用多个双矩阵对策来描述的两人多冲突环境,首先基于局中人所受到的约束条件,构造局中人面向多冲突环境的可行策略串集合;其次以所有局中人的可行策略串集合建立新的结局空间,构造局中人在新结局空间上的支付函数,进而建立两人多冲突环境下的对策综合集结模型;最后给出了一个军事例子,该算例说明了模型的实用性。

对Eric van Damme引理的再探讨

双矩阵博弈中的一个著名定理——完美均衡等价于非劣纳什均衡的证明依赖于van Damme给出的一个引理.有研究者认为,Damme引理的充分条件并不成立,其结果将导致定理有可能不成立.经过认真研究,得出的结论是,认为Damme引理不成立的理由并不充分,而是忽略掉了一个重要条件导致的结果,并对此进行了说明,并给出了Damme引理的一个严格证明.