基于免疫粒子群算法的广义Nash均衡问题求解

针对广义Nash均衡求解问题, 提出了一种免疫粒子群算法。首先利用非线性互补问题, 将广义Nash均衡问题转换为非线性方程组问题, 然后把免疫算法中抗体的免疫记忆功能和抗体浓度抑制机制引入基本粒子群算法, 设计了一种免疫粒子群算法。最后通过数值实验表明, 该算法保持了粒子群种群多样性, 增强了粒子群算法的全局寻优能力, 加快了算法的收敛速度, 具有较好的性能。     

一类n×n矩阵博弈的Nash均衡的近似计算

给出了一种求解某类n×n矩阵博弈Nash均衡的近似解的算法。通过剖分单纯形,将混合策略空间离散化,利用初始的单纯形根据标号函数和替换规则求出此类矩阵博弈Nash均衡的近似解。并分析了其最优解与近似解的计算误差。     

状态无关主动队列管理算法博弈的Nash均衡

状态无关的主动队列管理算法处理分组时不区分分组所在的流的信息,因此在Internet中,它易于设计和部署。文中通过数学分析和仿真方法研究AQM博弈和Nash均衡存在性。假设业务流是Poisson分布的且用户可自由修改发送速率,因而有结论:Drop Tail、RED不能获得Nash均衡,CHOKe可以获得近似Nash均衡。依据判定条件,推导出一种与状态无关且具有效率的Nash均衡AQM算法。     

一种面向多Agent交互的博弈Nash均衡求解方法

现有的图型博弈Nash均衡求解方法基本是在离散化剖面空间中搜索求解,最终只能得到近似Nash均衡。针对现有求解方法存在的不足,把求解图型博弈的Nash均衡看作是连续策略空间中的函数优化问题,定义Agents在策略剖面中的效用偏离度之和为优化目标,其最优解就是博弈的Nash均衡。本文基于对实例的分析指出目标函数下降梯度的计算可归结为一组线性规划,进而提出一种求解图型博弈Nash均衡的新型梯度下降算法。算法分析及实验研究表明,对于多Agent交互模型中的相关问题,本文提出的方法可求解任意图结构图型博弈Nash均衡,对于大规模图型博弈也有较好的求解精度和求解效率。     

不确定性下非合作博弈强Nash均衡的存在性

在已知不确定参数变化范围的假设下,研究了非合作博弈强Nash均衡的存在性问题.基于经典非合作博弈的强Berge均衡及帕雷托均衡的概念,结合非合作博弈NS均衡,定义了不确定性下非合作博弈的帕雷托强Berge和强Nash均衡的概念,并借助Ky Fan不等式证明其存在性.最后利用算例验证了其可行性和有效性.     

控制理论在电力电子学中的应用（下）——非线性与智能化方法

综述了电力是子学中的自适应控制,滑模变结构控制,神经网络控制及模糊控制,并对非线性控制及智能控制在电力电子学中的应用前景作出展望。     

非线性互补问题的光滑化拟牛顿算法

为求解非线性互补问题,给出了一种新的基于光滑对称扰动Fischer-Burmeister函数的光滑化拟牛顿算法。该算法利用了无导数线搜索。数值实验表明,算法是有效的。     

非线性互补问题的粒子群算法

针对非线性互补问题求解的困难,利用粒子群算法并结合极大熵函数法给出了该类问题的一种新的有效算法。该算法首先利用极大熵函数将非线性互补问题转化为一个无约束最优化问题,将该函数作为粒子群算法的适应值函数;然后应用粒子群算法来优化该问题。数值结果表明,该算法收敛快、数值稳定性较好,是求解非线性互补问题的一种有效算法。     

互补对称布尔函数的非线性度

互补对称布尔函数是一类特殊的对称布尔函数。在所有代数免疫最优的对称布尔函数中,有相当的比例均属此类函数。特别是当变元数量为2m元时,有2/3比例的代数免疫最优对称布尔函数都是互补对称布尔函数。通过布尔函数非线性度、Walsh谱和Krawtchouk多项式间的关系,计算出互补对称布尔函数的非线性度。结果表明,任意n元互补对称布尔函数的非线性度为2n-1-1/2[nn/2]     

On the supply function equilibrium and its applications in electricity markets

The paper deals with the Supply Function Equilibrium (SFE) as a model of competition in electricity markets. It introduces theoretical advancement through relaxing traditional assumptions of continuity of supply functions and provides a foundation for efficient computational algorithms. Two special examples are considered. One demonstrates that continuous equilibrium could be impossible while an infinite set of discontinuous equilibria exists. Another example proves the convergence to a linear equilibrium through learning in linear supply system. A possibility of a similar convergence for piece-wise linear system is being discussed.     

Bidding strategies in dynamic electricity markets

In this paper the problem of developing bidding strategies for the participants of dynamic oligopolistic electricity markets is studied. Attention is given to strategic bidding of load serving entities (LSE) in these markets. We model oligopolistic electricity markets as non-linear dynamical systems and use discrete-time Nash bidding strategies. We assume a Cournot model for our game, where the LSEs decide on demand quantities and the market price is the marginal cost of producing electricity.Attention is given to a problem, where the objective functions are quadratic in the deviations of trajectories from desired trajectories and quadratic in the control deviations from the nominal controls. It is assumed that each power marketer can estimate his/her competitors' benefit function coefficients.The optimal bidding strategies are developed mathematically using dynamic game theory. We deal with games that are non-linear in the state equations. We linearize these equations for complex non-linear oligopolistic electricity multi-markets and use discrete-time Nash strategies. We show that the actual dynamic excursions from the operating point where we linearize are small so that the linearization is valid. The developed algorithm is applied to an IEEE 14-bus power system. We show that the LSEs' expected profits are higher for our method than those for other methods in the literature (F. Wen, A.K. David, Optimal bidding strategies and modeling of imperfect information among competitive generators. IEEE Transactions on Power Systems, Vol. 16, No. 1, pp.15–21, Feb. 2001.     

A new SQP approach for nonlinear complementarity problems

Sequential quadratic programming (SQP) methods have been extensively studied to handle nonlinear programming problems. In this paper, a new SQP approach is employed to tackle nonlinear complementarity problems (NCPs). At each iterate, NCP conditions are divided into two parts. The inequalities and equations in NCP conditions, which are violated in the current iterate, are treated as the objective function, and the others act as constraints, which avoids finding a feasible initial point and feasible iterate points. NCP conditions are consequently transformed into a feasible nonlinear programming subproblem at each step. New SQP techniques are therefore successful in handling NCPs.     

A rolling horizon approach for stochastic mixed complementarity problems with endogenous learning: Application to natural gas markets

In this paper we present a new approach for solving energy market equilibria that is an extension of the classical Nash-Cournot approach. Specifically, besides allowing the market participants to decide on their own decision variables such as production, flows or the like, we allow them to compete in terms of adjusting the data in the problem such as scenario probabilities and costs, consistent with a dynamic, more realistic approach to these markets. Such a problem in its original form is very hard to solve given the product of terms involving decision-dependent data and the variables themselves. Moreover, in its more general form, the players can affect not only each others׳ objective functions but also the constraint sets of opponents making such a formulation a more complicated instance of generalized Nash problems. This new approach involves solving a sequence of stochastic mixed complementarity (MCP) problems where only partial foresight is used, i.e., a rolling horizon. Each stochastic MCP or roll, involves a look-ahead for a fixed number of time periods with learning on the part of the players to approximate the extended Nash paradigm. Such partial foresight stochastic MCPs also offer a realism advantage over more traditional perfect foresight formulations. Additionally, the rolling-horizon approach offers a computational advantage over scenario-reduction methods as is demonstrated with numerical tests on a natural gas market stochastic MCP. Lastly, we introduce a new concept, the Value of the Rolling Horizon (VoRH) to measure the closeness of different rolling horizon schemes to a perfect foresight benchmark and provide some numerical tests on it using a stylized natural gas market.     

A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market

This paper presents a simulation model based on the Nash equilibrium notion for the auction based day ahead electricity generation market. The presented model enhances a previous formalism proposed in the related literature by employing empirical data distributions of the market clearing price as registered by the market authority (e.g. the Independent System Operator). The model is effective when power suppliers with different generation capacities are considered, differently from the starting model that unrealistically assumes equal capacities. The proposed approach aims at evaluating the electricity market competitiveness with regard to the bidder strategies in order to prevent their anticompetitive actions. The framework is applied to a real data set regarding the Italian electricity market to enlighten its effectiveness in different scenarios, varying the number and capacity of participating bidders. The model can be employed as a basis for a decision support tool both for market participants (to define their optimal bidding strategy) and regulators (to avoid collusive strategies).     

Optimal price zones of electricity markets: a mixed-integer multilevel model and global solution approaches

Mathematical modelling of market design issues in liberalized electricity markets often leads to mixed-integer nonlinear multilevel optimization problems for which no general-purpose solvers exist and which are intractable in general. In this work, we consider the problem of splitting a market area into a given number of price zones such that the resulting market design yields welfare-optimal outcomes. This problem leads to a challenging multilevel model that contains a graph-partitioning problem with multi-commodity flow connectivity constraints and nonlinearities due to proper economic modelling. Furthermore, it has highly symmetric solutions. We develop different problem-tailored solution approaches. In particular, we present an extended Karush-Kuhn-Tucker (KKT) transformation approach as well as a generalized Benders approach that both yield globally optimal solutions. These methods, enhanced with techniques such as symmetry breaking and primal heuristics, are evaluated in detail on academic as well as on realistic instances. It turns out that our approaches lead to effective solution methods for the difficult optimization tasks presented here, where the problem-specific generalized Benders approach performs considerably better than the methods based on KKT transformation.     

The monotone convergence of a class of parallel nonlinear relaxation methods for nonlinear complementarity problems

We set up a class of parallel nonlinear multisplitting AOR methods by directly multisplitting the nonlinear mapping involved in the nonlinear complementarity problems. The different choices of the relaxation parameters can yield all the known and a lot of new relaxation methods, as well as a lot of new relaxed parallel nonlinear multisplitting methods for solving the nonlinear complementarity problems. The two-sided approximation properties and the influences on the convergence rates from the relaxation parameters about our new methods are shown, and sufficient conditions guaranteeing the methods to converge globally are discussed. Finally, a lot of numerical results show that our new methods are feasible and efficient.