Natural laminar flow shape optimization in transonic regime with competitive Nash game strategy

The Natural Laminar Flow (NLF) airfoil/wing design optimization is an efficient method which can reduce significantly turbulence skin friction by delaying transition location at high Reynolds numbers. However, the reduction of the friction drag is competitively balanced with the increase of shock wave induced drag in transonic regime. In this paper, a distributed Nash Evolutionary Algorithms (EAs) is presented and extended to multi-level parallel computing, namely multi-level parallel Nash EAs. The proposed improved methodology is used to solve NLF airfoil shape design optimization problem. It turns out that the optimization method developed in this paper can easily capture a Nash Equilibrium (NE) between transition delaying and wave drag increasing. Results of numerical experiments demonstrate that both wave drag and friction drag performances of a NE are greatly improved. Moreover, performance of the NE is equivalent to that of cooperative Pareto-optimum solutions, but it is more efficient in terms of CPU time. The successful application validates efficiency of algorithms in solving complex aerodynamic optimization problem.     

A Combined Evolutionary Search and Multilevel Optimisation Approach to Graph-Partitioning

The graph-partitioning problem is to divide a graph into several pieces so that the number of vertices in each piece is the same within some defined tolerance and the number of cut edges is minimised. Important applications of the problem arise, for example, in parallel processing where data sets need to be distributed across the memory of a parallel machine. Very effective heuristic algorithms have been developed for this problem which run in real-time, but it is not known how good the partitions are since the problem is, in general, NP-complete. This paper reports an evolutionary search algorithm for finding benchmark partitions. A distinctive feature is the use of a multilevel heuristic algorithm to provide an effective crossover. The technique is tested on several example graphs and it is demonstrated that our method can achieve extremely high quality partitions significantly better than those found by the state-of-the-art graph-partitioning packages.     

Embedding optimization in computational science workflows

Workflows support the automation of scientific processes, providing mechanisms that underpin modern computational science. They facilitate access to remote instruments, databases and parallel and distributed computers. Importantly, they allow software pipelines that perform multiple complex simulations (leveraging distributed platforms), with one simulation driving another. Such an environment is ideal for computational science experiments that require the evaluation of a range of different scenarios “in silico” in an attempt to find ones that optimize a particular outcome. However, in general, existing workflow tools do not incorporate optimization algorithms, and thus whilst users can specify simulation pipelines, they need to invoke the workflow as a stand-alone computation within an external optimization tool. Moreover, many existing workflow engines do not leverage parallel and distributed computers, making them unsuitable for executing computational science simulations. To solve this problem, we have developed a methodology for integrating optimization algorithms directly into workflows. We implement a range of generic actors for an existing workflow system called Kepler, and discuss how they can be combined in flexible ways to support various different design strategies. We illustrate the system by applying it to an existing bio-engineering design problem running on a Grid of distributed clusters.     

网络安全漏洞信息披露中多元参与主体行为策略演化博弈分析

全球网络威胁日趋严峻,作为网络安全事件产生的根源之一,网络安全漏洞越来越被重视,对漏洞的披露已是国家网络安全应急体系建设的重要内容之一。本文构建了网络安全漏洞共享平台、软件厂商及黑客之间的三方博弈模型,并从演化博弈角度对模型进行了分析和数值模拟,确立了稳定均衡点存在的条件。研究结果表明:不同参数初始值对三方博弈结果存在显著差异,软件质量越差,平台越倾向于“封闭披露”策略;对漏洞发现者的支付积极影响软件厂商“注册会员”倾向,对黑客的“努力攻击”倾向产生负面影响;随披露成本的上升,平台更倾向于采纳“封闭披露”策略;预期损失过大,软件厂商倾向于“注册会员”策略。     

Software Release Games

The software release game developed in Ref. 1 is reconsidered in the framework of a two-person nonzero-sum game of timing. More precisely, noisy-type software release strategies are derived in closed form under two different criteria as well as an alternative silent-type software release strategy. Our method overcomes the fatal problem in Ref. 1 and has an advantage on computational tractability. Also, the method can be extended directly to obtain noisy-type strategies.     

Implementation of parallel branch-and-bound algorithms – experiences with the graph partitioning problem

Parallel processing is one of the essential concepts in the attempts to increase the computational power available for solving continuous and discrete optimization problems. In the case where an optimization algorithm is search-based, crucial issues of parallel distributed implementations are work-load distribution and granularity, i.e. how to distribute the search space among processors and how to control the amount of processing between interprocessor communication. The present paper compares distributed implementations of two branch-and-bound algorithms for the graph partitioning problem: Given an undirected graph with an even number of edges and weights assigned to each edge, partition the vertices into two subsets of equal size such that the sum of the costs of edges connecting vertices in different subsets is as small as possible. The problem is known to be NP-complete. The two branch-and-bound methods compared differ in design strategy: One is based on time-consuming bound calculations leading to tight bounds and thus a narrow search tree with few nodes, whereas the other employs an easy bound calculation scheme leading to a larger search tree. Both have been implemented on an iPSC-hypercube with 32 processors. We investigate the influence of the design strategy on the performance of the algorithms.     

Participating life insurance policies: an accurate and efficient parallel software for COTS clusters

In this paper we discuss the development of a parallel software for the numerical simulation of Participating Life Insurance Policies in distributed environments. The main computational kernels in the mathematical models for the solution of the problem are multidimensional integrals and stochastic differential equations. The former is solved by means of Monte Carlo method combined with the Antithetic Variates variance reduction technique, while differential equations are approximated via a fully implicit, positivity-preserving, Euler method. The parallelization strategy we adopted relies on the parallelization of Monte Carlo algorithm. We implemented and tested the software on a PC Linux cluster.     

Distributed scheduling using simple learning machines

A new approach to develop parallel and distributed algorithms of scheduling tasks in parallel computers is proposed. A game theoretical model with the use of genetic-algorithms based learning machines called classifier systems as players in a game, serves as a theoretical framework of the approach. Experimental study of such a system shows its self-organizing features and the ability of collective behaviour. Following this approach a parallel and distributed scheduler is described. A simple version of the proposed scheduler has been implemented. Results of the experimental study of the scheduler demonstrate its high performance.     

两人进化博弈策略的决定机制与应用研究

通过建立一般情况下的两人进化博弈模型,给出了系统均衡点对应的矩阵行列式和迹表达式的经济含义,分析了16种典型情形下的进化稳定策略,详细讨论了均衡点稳定性分析结果所对应的博弈双方决策过程,从策略权衡的视角揭示了策略选择的内在机制。研究结果表明:不同策略前提下的相对净支付决定了系统的进化稳定策略,对方的策略选择以及自身可选策略的支付比较是影响博弈主体策略选择的两个基本要素,博弈主体会趋向于选择在对方策略既定下能够带来更大支付的策略。最后以环境治理中地方政府与企业以及地方政府之间的博弈关系为例,从对称博弈和非对称博弈两方面阐明了本文所构建模型在政策设计中的应用价值:针对不同案例,只要明确了两人博弈的支付矩阵,就可以通过计算相对净支付确定博弈双方的行为演化规律和稳定策略,从而简化计算过程,更加直接和更为便捷地为政策设计提供理论参考。     

The Superior Commander: A Methodology for the Control of Crisis Games

The Superior Commander is a new methodology for the design and control of experimental games in which the players may be exposed to crisis or stress. It satisfies three important requirements: the player in the game believes that he is an active decision maker; the game contains sufficient complexity and detail to be accepted by the player as realistic; and the experimenter is able to control the game so that he can replicate it exactly with different players. One implementation of the methodology, the Organisational Control Game, is described in detail, and the principles of the Superior Commander are demonstrated.The Superior Commander was developed to overcome some of the limitations of other gaming approaches to the study of decision making in crises, but it is also a general methodology for research games, and can be used for a range of purposes for which both realism and good experimental control are required.     

Nonlinear structural finite element analysis using the preconditioned Lanczos method on serial and parallel computers

The application of the Lanczos algorithm in Newton-like methods for solving non-linear systems of equations arising in nonlinear structural finite element analysis is presented. It is shown that with appropriate preconditioners iterative methods can be developed which are robust and efficient even for ill conditioned problems. Though the real advantage of iterative solvers seems to exist on distributed memory machines, even on serial machines the performance can be improved compared with direct solvers while saving memory capacity. With a specific modification of the Lanczos algorithm in combination with arc-length procedures a further speed-up of the nonlinear analysis can be achieved. For parallel implementations domain decomposition methods are used. A parallel preconditioning strategy based on an incomplete factorisation method is presented. An example is taken and the quality and efficiency of two different domain decomposition methods are discussed for a large shell structure. This work was supported by the BMBF (Bundesministerium für Bildung und Forschung) of Germany.     

基于iSIGHT平台的三维机翼气动优化设计

基于iSIGHT设计平台,结合CFD软件Fluent对三维机翼进行多目标优化设计,以提高其气动性能．设计过程中采用NCGA(neighborhood cultivation)——邻域培植遗传算法,NSGA-Ⅱ(non-dominated sorting)——非支配解排序遗传算法为优化算法,以N-S方程作为主控方程,对三维机翼优化．经过优化设计后结果表明,机翼的气动性能有了显著改善,该优化方法可推广用于多种翼型和机翼优化．     

基于质量安全投入的乳制品企业与经销商合作沟通机制形成的演化博弈分析

从我国乳制品企业和经销商尽快建立规范、高效的沟通机制的客观要求出发,本文探讨了乳制品企业和经销商之间的演化博弈关系。在阐述乳制品企业和经销商合作策略的基础上,基于有限理性,应用演化博弈模型分析基于质量安全投入的乳制品企业和经销商合作沟通机制形成的长期演化趋势,并运用Matlab 6.0软件对研究结果进行仿真分析。研究发现,降低经销商的“质量安全投入”成本、降低乳制品企业采取“建立沟通机制”所支付的成本、提高经销商的销售额外收益、增加在“质量安全不投入”策略下乳制品企业采取“不建立沟通机制”所多付出的损失成本、增加在“建立沟通机制”策略下经销商采取“质量安全不投入”所需缴纳的罚金、增加在“不建立沟通机制”策略下经销商给乳制品企业带来的损失,有助于乳制品企业和经销商合作沟通机制的形成。     

Experimental results on ultimatum games with incomplete information

This paper is about experiments on two versions of ultimatum games with incomplete information, called the offer game and the demand game. We apply the strategy method, that is, each subject had to design a complete strategy in advance instead of reacting spontaneously to a situation which occurs in the game. Game theory predicts very similar outcomes for the offer and the demand games. Our experiments, however, show significant differences in behavior between both games. Using the strategy method, allows us to explore the motivations leading to those differences. Since each subject played the same version of the game eight rounds against changing anonymous opponents we can also study subjects' learning behavior. We propose a theory of boundedly rational behavior, called the “anticipation philosophy”, which is well supported by the experimental data.     

A stochastic programming approach for supply chain network design under uncertainty

This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the sample average approximation (SAA) scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the stochastic model as well as the efficiency of the proposed solution strategy.     

A Parallel Multilevel Metaheuristic for Graph Partitioning

One significant problem of optimisation which occurs in many scientific areas is that of graph partitioning. Several heuristics, which pertain to high quality partitions, have been put forward. Multilevel schemes can in fact improve the quality of the solutions. However, the size of the graphs is very large in many applications, making it impossible to effectively explore the search space. In these cases, parallel processing becomes a very useful tool overcoming this problem. In this paper, we propose a new parallel algorithm which uses a hybrid heuristic within a multilevel scheme. It is able to obtain very high quality partitions and improvement on those obtained by other algorithms previously put forward.     

考虑延迟现象的质量改进投入和营销努力动态协同策略

利用时滞微分方程刻画质量改进投入对品牌商誉提升的延迟现象,分别构建了制造商和零售商采取非合作博弈、合作博弈以及成本分担的部分合作博弈(制造商参与营销的单向部分合作博弈、零售商参与生产的单向部分合作博弈、制造商参与营销及零售商参与生产的双向部分合作博弈)五种决策模式下的微分博弈模型。借助哈密尔顿极大值原理,求解得到五种情形下的制造商最优质量改进投入策略和零售商的最优营销努力策略以及供应链利润。对比五种博弈模式下的结果发现:1)延时现象会降低制造商进行质量改进投入的积极性,但对零售商营销努力无影响;品牌商誉在延迟现象影响下出现先衰减后提升的演进规律;2)合作博弈对于供应链绩效总是最优的,三种成本分担的部分合作博弈契约虽不能实现供应链的完全协调,但可以对非合作博弈情形进行帕累托改进;3)对比两种单向部分合作博弈,在提高供应链利润方面,制造商参与营销的成本分担契约优于零售商参与生产的成本分担契约;4)三种成本分担契约中,双向合作的部分合作博弈是供应链的最优选择,但随着延迟时间增大,其帕累托改进效果将不再明显。     

Game people play: Ayo

An analysis of Ayo is presented in this paper. The game is briefly described and it is shown that myopic decision guarantees solution to the game. The “odu” concept is discussed and it is shown that this strategy does not alter the solution methodology applied to solve the game without it. It is shown that the payoff matrix does not always have a saddle-point. Nevertheless, solution exists.     

Parallel linear multigrid by agglomeration for the acceleration of 3D compressible flow calculations on unstructured meshes

In this paper, we report on our recent efforts concerning the design of parallel linear multigrid algorithms for the acceleration of 3-dimensional compressible flow calculations. The multigrid strategy adopted in this study relies on a volume agglomeration principle for the construction of the coarse grids starting from a fine discretization of the computational domain. In the past, this strategy has mainly been studied in the 2-dimensional case for the solution of the Euler equations (see Lallemand et al. [6]), the laminar Navier–Stokes equations (see Mavriplis and Venkatakrishnan [12]) and the turbulent Navier–Stokes equations (see Carré [1], Mavriplis [10] and Francescatto and Dervieux [4]). A first extension to the 3-dimensional case is presented by Mavriplis and Venkatakrishnan in [13] and more recently in Mavriplis and Pirzadeh [11]. The main contribution of the present work is twofold: on the one hand, we demonstrate the successful extension and application of the multigrid by a volume agglomeration principle to the acceleration of complex 3-dimensional flow calculations on unstructured tetrahedral meshes and, on the other hand, we enhance further the efficiency of the methodology through its adaptation to parallel architectures. Moreover, a nontrivial aspect of this work is that the corresponding software developments are taking place in an existing industrial flow solver. This revised version was published online in June 2006 with corrections to the Cover Date.