摘要：简要介绍了系统科学中引人注目的复杂适应系统(CAS)理论,以及在美国桑塔费研究所开发的一个模拟工具集——swarm。swarm平台可以支持研究者对复杂适应系统使用多主体模拟(Multi-Agent Simulation)的方法来开展研究工作。本文还介绍了swarm的基本结构和工作原理,并且还结合一个实例简要阐述了模型的设计与实现过程,试图为复杂系统提供一个崭新的研究思路。

摘要：本文首先介绍了用swarm进行经济仿真的理论依据,接着又介绍了swarm是什么,它有什么样的特性,最后介绍了国内外的经济学家用swarm做了哪些工作。

摘要：1 背景文斯·卡斯特利是美国海军水面作战中心的一名研究人员,当他在美国北卡罗来纳州的海滨度假时,一群鹈鹕掠过海面捕食的情景让他不禁联想起他们拟议研制的一种价格便宜的小型无人机.当时他们的设想还很模糊,并没有具体的设计方案.于是,卡斯特利饶有兴趣地观察起来.他发现不时有一只鹈鹕冲入水中捕鱼,而其身后的空缺马上就被其它鹈鹕补上了.

摘要：与GIS集成是多智能体仿真平台的研究热点,是实现真实场景城市地理模拟的关键技术之一。探讨了降低智能体模型与GIS集成复杂性的体系架构,运用系统集成领域数据集成和应用集成的技术和方法,设计并实现了低耦合、简洁、实用的Java-swarm与GIS集成组件。该组件提供规范统一的空间数据访问、存储管理、生命周期管理、转换与映射等GIS数据集成服务,为城市模拟应用提供了组件化、低耦合、可配置的GIS集成应用方案。城市地理模拟模型开发实践证明,该组件的应用可以帮助开发人员降低GIS集成的技能要求、减少代码量、提高模拟模型开发效率和质量。

摘要：In this paper, we examine the problem of designing power system stabilizer (PSS). A new technique is developed using particle swarm optimization (PSO) combined with linear matrix inequality (LMI). The main feature of PSO, not sticking into a local minimum, is used to eliminate the conservativeness of designing a static output feedback (SOF) stabilizer within an iterative solution of LMIs. The technique is further extended to guarantee robustness against uncertainties wherein power systems operation is changing continuously due to load changes. Numerical simulation ahs illustrated the utility of the developed technique.

摘要：A Particle swarm Optimization (PSO) based design of three-phase induction motors are proposed. The induction motor design is treated as a non-linear and multivariable constrained optimization problem. The annual material cost and the total annual cost of the motor are chosen as two different objective functions. The PSO is used to find a set of optimal design variables of the motor which are then used to predict performance indices and the objective functions. The proposed method is demonstrated for two sample motors, and it is compared with the genetic algorithm (GA) and the conventional design methods. The results show that the PSO-based method effectively solved the induction motor design problems and outperforms the other methods in both the solution quality and computation efficiency.

摘要：This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.

摘要：流域生态是个复杂的巨系统,生态的治理需要各利益者的共同参与。应用复杂适应系统理论,对流域生态共同治理模式构建了swarm仿真框架,对模型中七类主体进行了分析,最后举例模拟说明了政府、排污企业和污水处理企业三类主体博弈时的过程。

摘要：swarm是一个多主体复杂适应系统(CAS)仿真建模软件平台。文中介绍了基于多主体系统的建模方法,并分析了swarm的基本原理。然后,利用swarm的标准工具集,设计了现代制造信息系统自组织过程模型的验证模型,并对该模型的构建过程进行了分析,试图为研究和使用swarm提供一些思路。

摘要：一、引言 利益相关者作为一个明确的理论概念是在1963年由斯坦福研究所(SRI, Stanford Research Institute)提出的.20世纪90年代以后, 随着以知识化、信息化、网络化和全球化为特征的新经济的到来, 传统的单边治理机制受到了巨大的挑战, 利益相关者共同治理机制成为现代企业治理的最优选择.