基于B细胞算法的克隆选择算法的收敛性分析

克隆选择算法(CSA)已经广泛应用于计算智能领域,而针对其理论方面的分析和研究工作相对较少。为了丰富克隆选择算法的理论基础,将含有多个体种群的克隆选择算法抽象为含单个体的B细胞算法（BCA）,简化了克隆选择算法的数学模型。给出了在BCA中使用的一种变异算子——连续区域超体变异算子（CRHO）和BCA的Markov链模型,提出了一个新的构造算法的状态跃迁矩阵的方法,证明了BCA的绝对收敛性。由于BCA是一般克隆选择算法的一种抽象,因此可以推断克隆选择算法的收敛性。     

细菌趋药性算法用于软硬件划分

引进细菌趋药性算法（BCA）,用于嵌入式系统和片上系统的软硬件双路划分。BCA是一种新型的对细菌觅食行为进行模拟的优化算法。对不同节点的控制数据流图进行仿真,表明在同等条件下,BCA收敛时间低于模拟退火算法、禁忌搜索与蚁群算法,节点规模越大,优势越明显。当节点规模高达100时,BCA消耗时间仅有常用优化算法的40%～60%。     

银行标准软件走向合作

Kordoba银行软件开发公司、SAP公司和西门子行业应用服务集团(SBS)三方签订了开发与营销合作协议,其目的是将SAP银行客户帐户解决方案(BCA)替换到Kordoba的银行后台核心系统中。新的Kordoba/BCA解决方案将提供全面的、跨平台的标准软件。 BCA适用于不同规模和不同业务的银行,是未来银行IT解决方案的基础。该后台核心系统将与Kordoba的应用系统如证券业务系统、支付交易系统等集成、以形成一个新的、完整的银行系统。     

折半聚类算法在基于社会力的人群疏散仿真中的应用

运用社会力模型（SFM）模拟人群疏散之前,需要先对人群进行聚类分组;然而,k中心聚类（k-medoids）和统计信息网格聚类（STING）这两大传统聚类算法,在聚类效率和准确率上都不能满足要求。针对这个问题,提出了折半聚类算法（BCA）。该算法结合了围绕中心点聚类和基于网格聚类两类方式,并利用二分法查找思想划分网格,不需要反复聚类。先将数据用二分法划分成网格,再根据网格内数据密度选出核心网格,接着以核心网格为中心将邻居网格聚类,最后按就近原则归并剩余网格。实验结果表明,在聚类时间上,BCA平均仅是STING算法的48.3%,不到k-medoids算法的14%;而在聚类准确率上,k-medoids算法平均仅是BCA的50%,STING算法平均也只是BCA的88%。因此,BCA无论在效率还是准确率上都明显优于STING和k-medoids算法。     

某些关系代数运算的n步并行处理

本文提出几个关系代数运算的n步并行处理模式。这些新的硬件算法都在同一拓朴结构的总线式细胞阵列(BCA)上完成。其时间复杂度减少到最小的n步,且与组的长度无关。电路的简单、规则化设计特别适宜于VLSI的实现,且引脚数与细胞数基本无关。     

CA互联互通示范工程中的目录服务建设

CA互联互通示范工程的目标是建立一个桥证书机构(BCA)，依据本工程提出的互联互通标准与规范，与国内已经建立的不同行业、不同地区、不同性质和体系各异的六个典型证书机构(CA)互联，实现不同CA间的互联互通和信任域扩展，为建设我国安全可信的外部环境做出示范。     

泰克推出最新高性能逻辑分析仪模块

2008年11月5日,全球领先的测试、测量和监测仪器提供商——泰克公司日前为泰克TLA7012便携式逻辑分析仪和TLA7016台式逻辑分析仪系列推出新的136通道TLA7BC4逻辑分析仪模块。新模块完善了最近推出的TLA7BBx模块。拥有20ps（50GS／s）的定时分辨率和高达128Mb的记录长度,新的TLA7BCA模块将是一系列一流嵌入式应用包括DDR3—1600、MIPI,以及如英特尔QuickPath互连技术等尖端计算机应用的理想解决方案。     

一般克隆选择算法的收敛性证明*

克隆选择算法已经广泛应用于计算智能领域,而针对克隆选择算法理论方面的分析和研究工作却很少。为了丰富克隆选择算法的理论基础,采用了与研究遗传算法相似的方法,研究了克隆选择算法的收敛性,推导出克隆选择算法在求解优化问题时,收敛到全局最优解的充分条件。因此,对基于克隆选择算法的各种应用如BCA和CLONALG算法,只要检查这些充分条件是否满足就可以证明算法的收敛性。     

基于反向学习与Levy飞行的改进蜂群算法

为了优化蜂群算法(BCA),平衡局部搜索与全局搜索,避免算法陷入局部最优,并提高蜂群算法的收敛速度,提出了一种多策略改进的方法优化蜂群算法(MSO-BCA).算法在种群初始化阶段采用了反向学习(OBL)初始化的方法;在种群更新与邻域搜索中采用了具有Levy飞行特征的改进搜索策略.经过对经典Benchmark函数的反复实验并与其他算法的比较,表明了所提出的算法具有良好的加速和收敛效果,提高了全局搜索能力与效率.     

基于多核模糊粗糙集与蝗虫优化算法的高光谱波段选择

波段选择能有效减少高光谱数据的空间冗余,为后续分类提供有效的支持。多核模糊粗糙集模型能够对包含不确定性的数值数据进行分析和近似描述,而蝗虫优化算法对优化问题求解具有较强的探索和开发能力,因而将多核模糊粗糙集模型引入高光谱的不确定性分析建模中,采用蝗虫优化算法对波段子集进行选择,提出了一种基于多核模糊粗糙集与蝗虫优化算法的高光谱波段选择算法。首先,使用多核算子来进行相似性度量,提高模型对数据分布的适应性。定义基于核模糊粗糙集的波段相关性度量,通过模糊粗糙集中不同像素点地物上的下近似分布来度量波段之间的相关性。然后,综合考虑波段依赖度、波段信息熵、波段间相关性来定义波段子集的适应度函数。最后,在常用高光谱数据集Indiana Pines农业区上,采用J48和K近邻(KNN)作为分类算法,把所提算法与波段相关性分析(BCA)、标准化互信息(NMI)算法进行分类性能比较。实验结果表明,在选取较少波段个数时,所提算法的总体平均分类精度提高了2.46和1.54个百分点。     

Improvement of borrowing channel assignment for patterned traffic load by online cellular probabilistic self-organizing map

This paper describes an improvement of borrowing channel assignment (BCA) for patterned traffic load by using the short-term traffic prediction ability of cellular probabilistic self-organizing map (CPSOM). The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques today. In case of patterned traffic load, the traditional BCA methods are not efficient to further enhance the performance because heavy-traffic cells cannot borrow channels from their neighboring cells with light or medium traffic that may have unused nominal channels. The performance can be increased if the short-term traffic load can be predicted. The predicted results can then be used for channel re-assignment. Therefore, the unused nominal channels of the light-or-medium-traffic cells can be transferred to the heavy-traffic cells that need more nominal channels. In this paper, CPSOM is used online for traffic prediction. In this sense, the proposed CPSOM-based BCA method is able to enhance the performance for patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method enables the system to work with better performance for patterned traffic load than the traditional BCA methods.     

Using Cellular Probabilistic Self-Organizing Map in Borrowing Channel Assignment for Patterned Traffic Load

The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques. Borrowing channel assignment (BCA) is a compromising technique between fixed channel allocation (FCA) and dynamic channel allocation (DCA). However, in the case of patterned traffic load, BCA is not efficient to further enhance the performance because some heavy-traffic cells are unable to borrow channels from neighboring cells that do not have unused nominal channels. The performance of the whole system can be raised if the short-term traffic load can be predicted and the nominal channels can be re-assigned for all cells. This paper describes an improved BCA scheme using traffic load prediction. The prediction is obtained by using the short-term forecasting ability of cellular probabilistic self-organizing map (CPSOM). This paper shows that the proposed CPSOM-based BCA method is able to enhance the performance of patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method delivers significantly better performance than BCA for patterned traffic load situations, and is virtually as good as BCA in the other situations analyzed.     

“Real-time” equivalence of cellular automata and linear-bounded automata

It is shown that a one-dimensional bounded cellular automaton (BCA) can simulate a linear-bounded automaton (LBA) in essentially real time, and that, conversely, an LBA can simulate a BCA, on an input of length n, in just n transitions of the LBA per BCA transition.     

Borrowing channel assignment strategies based on heuristictechniques for cellular systems

The demand for more efficient and fast channel allocation techniques in cellular systems increases day by day. Borrowing channel assignment (BCA) was introduced in the literature as a compromise between the classic fixed and dynamic channel allocation schemes. This paper examines the behavior of three heuristic BCA techniques based alternatively on a Hopfield neural network, an efficient evolutionary algorithm named combinatorial evolution strategy (CES) and a third heuristic which combines the basic advantages of the two above computational intelligence methods. By considering some specific assumptions that follows an ideal cellular mobile model, BCA is formulated as a combinatorial optimization problem. The above heuristics have been extensively applied to solve efficiently such problems in the past. Simulation results, derived for uniform and nonuniform traffic load conditions, are used to compare these BCA schemes each other as also with other well-established allocation techniques.     

A distributed agent system for port planning and scheduling

This paper describes a distributed agent system for dynamic port planning and scheduling. The proposed system comprises four agents, namely a port planning manager (PPM), a berth control agent (BCA), a shuttle allocation agent (SAA) and a yard storage agent (YSA). These agents communicate and cooperate with one another to work out the schedule for berth allocation and requirements for shuttles. Basically, the PPM maintains all the necessary information and provides a negotiation and communication locale for the BCA, SAA and YSA, which represent the berths, the shuttles and the container yard, respectively, to resolve their conflicts under its supervision. With the help of a GA-enhanced dynamic scheduler, the BCA attempts to optimise the berth allocation schedule using the data such as ship arrival information retrieved from the PPM. As soon as the SAA receives the schedule from the BCA, it proceeds to check for the availability of shuttles and work out the requirement for shuttles. On the other hand, the YSA generates the yard storage allocation schedule and the truck/train schedule for the transportation of containers.A case study is conducted to illustrate the capability of the distributed agent system. It has been shown that the proposed system is able to successfully generate the schedules for both the BCA and the SAA.