Modeling and convergence analysis of distributed coevolutionary algorithms

A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyzes are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior.     

协同演化算法研究进展

协同演化算法(coevolutionary algorithms,CEA)是当前国际上计算智能研究的一个热点,它运用生物协同演化的思想,是针对演化算法的不足而兴起的,通过构造两个或多个种群,建立它们之间的竞争或合作关系,多个种群通过相互作用来提高各自性能,适应复杂系统的动态演化环境,以达到种群优化的目的.介绍了协同演化算法的研究状况以及目前的研究进展,概述了它的基本算法、主要特点、理论与技术,同时介绍了一些主要的应用领域,指出了协同演化算法的研究方向.     

邻域规模对共生进化算法搜索性能的影响

共生进化算法求解复杂组合问题时表现了良好的性能,其选择邻域实现局部进化。对于复杂的的柔性作业调度组合问题,作业调度结果的好坏首先依赖流程设计的质量。以共生进化算法求解复杂柔性作业调度为例,测试不同邻域规模对共生进化算法搜索性能的影响。仿真结果表明,局部进化邻域规模的大小对共生进化算法在平均求解质量及对最优解的逼近能力两个方面均没有显著影响,过大的局部进化邻域会增加算法中排序操作计算量。     

虚拟运行环境描述管理工具的研究和实现

针对分布式网络环境中虚拟运行环境的描述、部署和管理问题,设计和实现了一个图形化的虚拟运行环境的描述、部署和管理系统,该系统整合虚拟机技术,通过图形化的方式来描述目标虚拟运行环境的网络拓扑,配置和部署环境中的虚拟机,并提供了对其中虚拟机基本的管理和控制功能.具有通用性、高可扩展性和灵活性的特点.可用于网格以及其它分布式系统.     

A deep recurrent Q network towards self-adapting distributed microservice architecture

One desired aspect of microservice architecture is the ability to self-adapt its own architecture and behavior in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements distributed microservice architecture model running a swarm cluster, as informed by the Monitor, Analyze, Plan, and Execute over a shared Knowledge (MAPE-K) model. The proposed architecture employs multiadaptation agents supported by a centralized controller, which can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-learning network (DRQN). It is argued that such integration between DRQN and Markov decision process (MDP) agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resource overprovisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms, including (1) a deep Q-learning network (DQN), (2) a dueling DQN (DDQN), (3) a policy gradient neural network, and (4) deep deterministic policy gradient. The DRQN implementation in this paper manages to outperform the aforementioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training time. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms.     

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

The existing algorithms to solve dynamic multiobjective optimization (DMO) problems generally have difficulties in non-uniformity, local optimality and non-convergence. Based on artificial immune system, quantum evolutionary computing and the strategy of co-evolution, a quantum immune clonal coevolutionary algorithm (QICCA) is proposed to solve DMO problems. The algorithm adopts entire cloning and evolves the theory of quantum to design a quantum updating operation, which improves the searching ability of the algorithm. Moreover, coevolutionary strategy is incorporated in global operation and coevolutionary competitive operation and coevolutionary cooperative operation are designed to improve the uniformity, the diversity and the convergence performance of the solutions. The results on test problems and performance metrics compared with ICADMO and DBM suggest that QICCA has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.     

Decoupled optimal design for power electronic circuits with adaptive migration in coevolutionary environment

This paper presents a genetic based decoupled optimal design method for power electronics circuit design using an adaptive collaboration approach in a cooperative coevolutionary environment. The circuit parameters of the power conversion stage and the feedback network of a buck regulator are optimized through two parallel coadaptive genetic based optimization processes. The best candidate of the tunable parameters in one evolutionary process for the design of the power conversion stage is merged to the other evolutionary process for the design of the feedback network as untunable factors through a collaboration controller in which the collaboration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value of the genes representing the circuit design parameters in each generation. The proposed design methodology is suitable for parallel computation resulting in considerable improvement in searching efficiency. Simulated results of the design of a buck regulator with the proposed approach were verified with experimental results from the actual hardware implementation. It showed that the design with the proposed scheme was compatible with the design specification.     

Solution of multiobjective optimization problems: coevolutionary algorithm based on evolutionary game theory

When attempting to solve multiobjective optimization problems (MOPs) using evolutionary algorithms, the Pareto genetic algorithm (GA) has now become a standard of sorts. After its introduction, this approach was further developed and led to many applications. All of these approaches are based on Pareto ranking and use the fitness sharing function to keep diversity. On the other hand, the scheme for solving MOPs presented by Nash introduced the notion of Nash equilibrium and aimed at solving MOPs that originated from evolutionary game theory and economics. Since the concept of Nash Equilibrium was introduced, game theorists have attempted to formalize aspects of the evolutionary equilibrium. Nash genetic algorithm (Nash GA) is the idea to bring together genetic algorithms and Nash strategy. The aim of this algorithm is to find the Nash equilibrium through the genetic process. Another central achievement of evolutionary game theory is the introduction of a method by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an evolutionary stable strategy (ESS). In this article, we find the ESS as a solution of MOPs using a coevolutionary algorithm based on evolutionary game theory. By applying newly designed coevolutionary algorithms to several MOPs, we can confirm that evolutionary game theory can be embodied by the coevolutionary algorithm and this coevolutionary algorithm can find optimal equilibrium points as solutions for an MOP. We also show the optimization performance of the co-evolutionary algorithm based on evolutionary game theory by applying this model to several MOPs and comparing the solutions with those of previous evolutionary optimization models. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

Models for network design,servicing and monitoring of ATM networks based on the virtual path concept

Virtual Path (VP) concept has gained attention in terms of effective deployment of ATM networks for wide-area environment. In this paper, we present models and algorithms for network design and management of ATM networks based on virtual path concept from a network planning perspective. Our approach is based on statistical multiplexing of traffic within a traffic class by using a virtual path for the class and deterministic multiplexing of different virtual paths, and on providing dynamic bandwidth and reconfigurability through virtual path concept depending on traffic load during the course of the day. For some realistic example networks, we observe that considering network dynamism through variation of traffic during the course of a day by doing dynamic bandwidth and virtual path reconfiguration can save between 10 to 14% in network design costs compared to a static network based on maximum busy hour traffic.     

A Tournament-Based Competitive Coevolutionary Algorithm

For an efficient competitive coevolutionary algorithm, it is important that competing populations be capable of maintaining a coevolutionary balance and hence, continuing evolutionary arms race to increase the levels of complexity. We propose a competitive coevolutionary algorithm that combines the strategies of neighborhood-based evolution, entry fee exchange tournament competition (EFE-TC) and localized elitism. An emphasis is placed on analyzing the effects of these strategies on the performance of competitive coevolutionary algorithms. We have tested the proposed algorithm with two adversarial problems: sorting network and Nim game problems that have different characteristics. The experimental results show that the interacting effects of the strategies appear to promote a balanced evolution between host and parasite populations, which naturally leads them to keep on evolutionary arms race. Consequently, the proposed algorithm provides good quality solutions with a little computation time.     

A distributed Cooperative coevolutionary algorithm for multiobjective optimization

Recent advances in evolutionary algorithms show that coevolutionary architectures are effective ways to broaden the use of traditional evolutionary algorithms. This paper presents a cooperative coevolutionary algorithm (CCEA) for multiobjective optimization, which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subpopulations. Incorporated with various features like archiving, dynamic sharing, and extending operator, the CCEA is capable of maintaining archive diversity in the evolution and distributing the solutions uniformly along the Pareto front. Exploiting the inherent parallelism of cooperative coevolution, the CCEA can be formulated into a distributed cooperative coevolutionary algorithm (DCCEA) suitable for concurrent processing that allows inter-communication of subpopulations residing in networked computers, and hence expedites the computational speed by sharing the workload among multiple computers. Simulation results show that the CCEA is competitive in finding the tradeoff solutions, and the DCCEA can effectively reduce the simulation runtime without sacrificing the performance of CCEA as the number of peers is increased.     

高校办公网入侵检测系统研究

随着计算机和网络技术的发展,网络入侵事件的日益增加,人们发现只从防御的角度构造安全系统是不够的,入侵检测成为继“防火墙”、“数据加密”等传统安全保护措施后新一代的网络安全保障技术。本文首先介绍入侵检测原理和分布式入侵检测方面的相关工作,在分析已有分布式入侵检测系统模型的基础上,提出了一个基于代理的校园网入侵检测系统模型框架。该模型采用分布式的体系结构,由一个代理控制中心和若干代理组成,结合了基于网络和基于主机的入侵检测方法。使用代理技术在分布式环境下对入侵进行检测,可以有效地检测各种入侵,并具有很好的可扩充性。     

Online algorithms supporting emergence in distributed CAD systems

The rule-based approach from traditional AI and the conventional constraint satisfaction algorithms are not adequate to cope with the unpredictable events and interactive computations in distributed CAD environments. This paper claims that the problems faced by distributed CAD systems require solutions based on the concepts of emergence, reactivity, and online algorithms. The present paper presents Extended Constraints Graphs (ECGs) as online algorithms supporting emergence in a network of reactive agents. ECGs represent an effective solution for the nonlinear constraint problem in cyclic graphs, which are distributed over a heterogeneous computer network. The relationships between agents in an ECG are generic, distributed, recursive and nonlinear — a problem not solved in the literature. Also this paper presents a CORBA model integrated with a Geometry Bus to support distributed virtual prototype whose variables can be defined in terms of ECGs.     

Supporting evolution in a multi-agent cooperative design environment

New architectures for next-generation of computer-aided design environments need to be distributed, evolutive, and cooperative. This paper presents a multi-agent design environment that supports cooperative and evolutionary design by cooperation of a group of agents. Multi-agent system architecture offers a promising framework with its novel approach for dynamically creating and managing agent communities in widely distributed and ever-changing design environments; while the genetic algorithm based approach provides a foundation for supporting evolutionary and innovative design abilities. The computational and information processing processes involved in evolutionary and distributed environment are discussed in this paper.     

基于分布对象的并行程序设计方法研究

研究分布式对象的并行实现及优化,提出一种基于分布式对象的并行程序设计方法,构建一个基于分布式对象的并行程序设计模型,并以此方法完成虚拟计算机网络实验系统的设计和实现实验结果表明,该虚拟计算机网络实验系统并行性较好、响应速度适中,证明基于分布式对象的并行程序设计方法在改善微机系统并行性上具有一定的作用     

A simulation of an economic,self-organising resource allocation approach for application layer networks

Application Layer Networks like Utility Grid Networks or Cloud Computing Systems, often depicted as large scale self-organising architectures using a shared infrastructure, will need innovative management approaches for controlling and matching services demand and supply. This article shows a self-organising resource allocation approach based on principles of the Catallaxy, an economic concept of F.A. von Hayek. The implementation uses a coevolutionary learning algorithm for adaptation of the agent strategy to the dynamic Application Layer Network environment. The simulation study performs a sensitivity analysis of a large scale Application Layer Network scenario with 2000 agents. A set of representative agents is selected and their evolutionary behaviour analysed.     

e-CAPP: A distributed collaborative system for internet-based process planning

Advances in distributed technologies have enabled engineers to communicate more effectively, collaborate, obtain, and exchange a wide range of design resources during product development. Shared internet-based virtual environments allow experts in remote locations to analyze a virtual prototype, together and simultaneously in centers in which the product is being developed.This paper presents a system for distributed and collaborative environment which could assist manufacturing enterprises and experts in discussing, suggesting, evaluating and selecting best process plans for family of manufacturing parts. The represented e-CAPP system enables the implementation of expert knowledge in an appropriate knowledge repository. The knowledge from this repository is integrated into intra-company CAPP systems and used while generating process plans for new products. The proposed internet-based collaborative environment, dedicated to distributed process planning, is yet another step in the direction advancing of distributed manufacturing.     

面向分布式虚拟设计的协同工作环境研究

为了实现分布式虚拟设计过程中的信息共享、协作以及冲突消解,建立了一个面向分布式虚拟设计的协同工作环境．描述了系统体系模型的研究方法和基于CORBA的多层分布式结构,详细介绍了基于TCP／IP协议和基于面向对象的运行时期类型信息(Run Time Type Information,RTTI)技术的实体实时通信协议的技术内涵、执行流程和设计实现,该协议用于保持网络中不同结点的几何模型在实体级别上的一致性．对分布式虚拟设计中的并发性问题进行了探讨,采用实体锁定机制实现了某些环节的冲突消解,并对几种实体锁定的仲裁方法进行了比较．     

一种基于兴趣约束的新型组播地址分配方法

基于兴趣的约束关系提出了一种新型的组播地址分配方法——建立布种模型，通过布种模型的初始化和推演实现组播地址的静态和动态分配．论文结合二维点阵和PR四分树定义了布种模型的空间数据结构，并提出自适应生长／剪枝算法和检索算法实现组播地址的动态分配和快速搜索．算法分析和性能测试表明该方法效率高，可满足大规模分布式虚拟环境中的组播地址分配和检索．最后简介了该方法在分布式仿真运行平台BH RTI中的实现．     

基于网络远程数据采集的虚拟仪器实验系统构建

与传统的实验系统相比,基于网络远程数据采集的虚拟实验系统,能降低实验室建设成本,它使课堂教学更形象、生动,能更好地实现理论教学与实践相结合。该文提出基于DataSocket远程数据采集技术,构建了多层分布式网络远程数据采集体系结构模式。通过虚拟议器设计,在电子线路实验中得以应用,有较好的效果。