Optimization of fused fuzzy systems via genetic algorithms

In this paper, the problem of optimization of fused fuzzy systems via genetic algorithms is addressed. The proposed architecture separates a large fuzzy rule optimization problem into a series of more manageable and smaller problems through rule-table transformation. The algorithm is applied to the cart-pole system to demonstrate its characteristics     

资源系统的优化分析与实践

资源系统建设早、架构老、需求管控缺少统一规划等问题的长期累积,导致系统性能降低,当有集中、大批量操作时,性能明显不足,会出现一线人员无法受理、装维人员无法施工等问题,直接影响用户感知,现场调研一线人员也多次反映该问题,亟待成立专项小组,集中力量对其进行优化。经过多次分析评估,最终采用新建统计库承接生产库部分功能、调整软硬件配置参数、软件程序代码走查优化、数据库长事务及大表优化等4项重要措施对资源系统进行优化与改进,增强了系统的健壮性,提升了其对用户、一线人员的支撑能力。     

基于DSP平台的AVS视频编码器设计优化

介绍了在TMs320DM6446 DSP平台上实现AVS视频编码器的算法设计与优化方法.在软件整体设计优化的基础上,重点对运动估计等算法进行了优化改进;同时针对平台特点给出结构优化方法.主要包括提高代码并行性及存储器和数据搬移的优化.测试结果表明,通过优化,在保证图像质量损失较小的情况下,编码器的编码速率有显著提高.     

机载SDR系统SCA核心框架优化技术

针对软件通信架构(Software Communication Architecture,SCA)核心框架在机载领域臃肿和低效等问题,提出了一种面向机载软件无线电(Software Defined Radio,SDR)系统的SCA核心框架优化方案.首先,为使架构轻量高效,提出功能单元裁剪方法;其次,针对时间优化问题,分析了SCA的局限性,提出可灵活配置的加载流程优化技术,在兼容SCA规范的基础上可根据实际系统特点实施灵活的组件加载,充分发挥系统的并行加载潜力来缩短加载时间.实验结果表明,所提的优化技术能够有效提升SDR系统可用性,对面向机载SDR系统的SCA架构优化设计和应用具有较强的指导意义.     

网络攻击检测中基于RTFF的大容量数据管理

In this paper, we present Real-Time Flow Filter (RTFF) —a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet in-spection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on ob-served attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.     

Multiple Weighted Objectives Heuristic for the Redundancy Allocation Problem

A new heuristic is proposed and tested for system reliability optimization. The multiple weighted objective heuristic is based on a transformation of the problem into a multiple objective optimization problem, and then ultimately, transformation into a different single objective problem. The multiple objectives are to simultaneously maximize the reliability of each individual subsystem. This is a logical approach because system reliability is the product of the subsystem reliabilities, so if they are maximized, the system reliability will also be high. This new formulation and associated heuristic are then based on solving a sequence of linear programming problems. It is one of the very few optimization approaches that allow for linear programming algorithms and software to be used for the redundancy allocation problem when mixing of functionally equivalent components is allowed. Thus, it represents an efficient solution method that relies on readily available optimization tools. The heuristic is tested on many example problems, and compared to competing solution approaches. Overall, the heuristic performance is observed to be very good on the tested problem, and superior to the max-min heuristic regarding both efficiency, and performance.     

先锋遗传算法在多峰值函数优化中的应用

本文对遗传算法应用于多峰值数值函数优化进行了研究,通过对算法的综合和改进,克服了传统遗传算法计算多峰值函数时容易出现的遗传漂移问题,实现了优化目的,证实了先锋遗传算法应用于此类问题的可行性和有效性.     

基于遗传算法的光电干扰资源优化分配研究

结合光电信息系统作战运筹中目标分配原则，及军事运筹理论，建立了干扰资源优化分配模型。给出了基于遗传算法的模型求解方法及步骤，阐述了遗传算法在光电干扰资源分配中的应用，并进行了实例仿真，得出了最佳干扰资源分配方案。仿真结果证明，该方法对于约束条件复杂的优化模型求解是有效、可行的，为光电信息系统指挥控制决策提供了有力支持。     

GPS L1信号C/A码的高效并行软件相关器研究

在GPS软件接收机的捕获和跟踪算法中,相关器的运算是很大的计算负担,为了能够快速的计算,提出了一套用于GPS软件接收机的,处理CDMA扩频信号的软件相关器高效并行算法,主要针对耗时较多相关器进行算法优化,并给出了实际运行结果和性能分析,表明了本文提出的优化算法与直接浮点计算的方法相比,将软件相关器的处理速度提高了约4倍。而且,运用高效并行软件相关器的跟踪算法能够正确跟踪,并解调信号。介绍的相关器的设计方法适用于所有卫星导航系统的软件接收机。     

免疫遗传算法及认知无线电参数优化决策

根据感知的频谱环境变化及时优化并调整无线电参数是认知无线电的关键技术之一,也是一个复杂的非线性多目标优化决策问题。遗传算法是最适合优化问题的,但当遗传算法应用于优化问题时存在过早收敛问题。提出了基于遗传算法和人工免疫系统相结合的免疫遗传算法（IGA）来克服以上问题。由于在GA算法中引入了免疫系统中抗体和抗原的概念并在每一次迭代中丢弃亲和力较大的抗体,有效地防止了GA中过早收敛现象。最后,用免疫遗传算法来解决认知无线电的参数优化问题。仿真结果表明,免疫遗传算法可以迅速达到最优决策。     

基于自适应截断策略的约束多目标优化算法

为提高约束多目标优化问题所求解集的分布性和收敛性,该文提出基于自适应截断策略的约束多目标优化算法。首先,自适应截断选择策略能够保留Pareto最优解和约束违反度及目标函数值均较优的不可行解,不仅提高了种群多样性,而且能够较好地兼顾多样性和收敛性;其次,为增强算法的局部开发能力,在变异操作和交叉操作之后进行指数变异;最后,改进的拥挤密度估计方式只选择一部分Pareto最优解和距离较近的个体参与计算,不仅更加准确地反映解集的分布性,而且降低了计算量。通过在标准测试问题(CTP系列)上与其他4种优秀算法的对比结果可以得出,该算法所求解集的分布性和收敛性均得到一定提高,而且相较于对比算法在求解性能上具备一定的优势。     

Hill-climbing heuristics for optimal hardware dimensioning andsoftware allocation in fault-tolerant distributed systems

An optimum distributed architecture with fault-tolerance capabilities for a given software application may be obtained by allowing allocation algorithms to evolve without any existing-hardware constraint. Distributed software partitioning and allocation is done using the simulated annealing optimization algorithm. To define the cost function used by the optimization algorithm, a model for interacting processes constituting the software application is presented. Tuning of algorithm parameters has been considered to assure convergence at a reasonable cost in terms of computation time     

A tutorial on using genetic algorithms for the design of network topology

The design of network topology is an important part of network design, since network topology is directly associated with network operational behavior, capacity, reliability, and cost. This paper is a tutorial paper concerned with illustrating how the optimization capabilities of genetic algorithms can be used to design suitable network topologies considering basic topology problems. Simple genetic algorithms have been developed for the topology problem of mesh networks, considering single node and single link failure tolerance. The algorithms are based on criteria of two important measures: minimizing the length of communication links; and minimizing traffic flow through these links for given traffic loads. The first measure contributes to minimizing the cost of cabling, while the second measure contributes to minimizing the cost of link capacity. The work provides a useful approach and tools to network students and professionals concerned with the topology design of backbone networks. The developed software is made available on the Internet. Copyright © 2006 John Wiley & Sons, Ltd.     

The Design of an Interactive Computer Software System for the Frequency-Assignment Problem

This paper discusses the problem of finding an interferencefree frequency assignment in a large communication system (e. g., in a large Naval fleet). In particular, the problem scenario, and the two algorithms, one an optimization procedure, and the second a heuristic technique, are discussed. The optimization algorithm is of the enumerative type and, in scenarios with a large communication system, may not terminate within a reasonable amount of computer time. To overcome this difficulty, an interactive heuristic-based software system is described which can be used by an astute decision maker to obtain a usable assignment relatively quickly.     

Optimization of UPFCs using hierarchical multi-objective optimization algorithms

Optimal location, number, and settings of unified power flow controllers (UPFC) using various multi-objective optimization algorithms is presented in this paper. The UPFC parameters, locations and number are computed to maximize the voltage stability margin and minimize the real power losses at the same time. For this, developed hierarchical optimization versions of three recent multi-objective algorithms are proposed namely: non-dominated genetic algorithms (NSGA-II), non-dominated sorting particle swarm optimization (NSPSO) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The fuzzy logic is proposed to extract the best compromise solution from the Pareto set. The proposed algorithms are applied to IEEE 30-bus power system. The line flow and load bus voltage limits are taken into account. The obtained results show that the installation of the UPFC in the power system minimizes the power losses, enhances the static voltage stability, and improves the voltage profiles. Furthermore, the proposed methods are able to solve a hard discrete?Ccontinuous constrained multi-objective optimization problem. In addition, they do not show any limitation on the number of objective functions under consideration.     

电视发射台集中监控系统设计

针对电视发射台设备监控的实际需求,提出了基于Web的监控方案,讨论了监控系统的体系结构及各层的实现方法,解决了多种类型设备的集中监控以及细粒度访问控制等问题。实践证明,该方案能满足大型电视发射台设备集中监控的要求。     

噪声环境下遗传算法的收敛性和收敛速度估计

 问题求解的环境往往非常复杂,不确定的环境因素、人为因素等都可导致问题处于噪声环境,从而影响实际优化问题的目标函数值的评价.噪声环境下遗传算法的研究在国内外均起步较晚,特别是收敛性和收敛速度的分析是该领域急待解决的问题.本文根据优胜劣汰遗传算法的特性,基于吸收态Markov链的数学模型证明了噪声环境下优胜劣汰遗传算法的收敛性,提出了噪声环境下优胜劣汰遗传算法的首达最优解期望时间的估算方法.     

Cloud Based Software Computing as a Service in Hybrid Evolution Algorithm with Feedback Assistance

This paper develops cloud based software computing as a service (SCaaS) in the hybrid evolution algorithm with feedback assistance to solve the data optimal NP-complete problems such as travel salesman problems and job shop scheduling problems. Suitable steps, methods, or procedures of the genetic algorithm can be adopted from various evolution procedures or methods of the genetic algorithms based on the fitness evaluation results and survival ratio of different crossover methods in the current generation. The proposed system can dynamically emphasize the corresponding methods or procedures for the better performance in optimal solution searching. In addition, according to the proposed XML format, system users can upload only the coding of chromosomes without implementing the genetic algorithm program. Furthermore, by using the feedback assistance, the convergence time of the optimal solution can be enhanced.     

分层异构信号处理平台调度方法研究

异构多处理器的高效性和可靠性能够满足日趋复杂的信号处理任务需求,因此分层异构系统已成为信号处理平台的发展趋势.为提高平台强实时性并解决高吞吐量的问题,文中对分层异构信号处理平台的软硬件模块及架构进行了研究,并采用有向无环图对组件任务及硬件资源进行建模.将已提出的调度算法按照任务类型、调度目标、调度过程和研究方法进行分类...     

Density PSO-based software module clustering algorithm

Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution, density PSO-based (DPSO) software module clustering algorithm is proposed. Firstly, the software system is converted into complex network diagram, and then the particle swarm optimization (PSO) algorithm is improved. The shortest path method is used to initialize the swarm and the probability selection approach is used to update the particle positions. Furthermore, density-based modularization quality (DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of the DPSO algorithm. Hill climbing (HC) algorithm, genetic algorithm (GA), PSO and DPSO algorithm are compared in the modularization quality (MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than other traditional three algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.