An improved NEH-based heuristic for the permutation flowshop problem

NEH is an effective heuristic for solving the permutation flowshop problem with the objective of makespan. It includes two phases: generate an initial sequence and then construct a solution. The initial sequence is studied and a strategy is proposed to solve job insertion ties which may arise in the construct process. The initial sequence which is generated by combining the average processing time of jobs and their standard deviations shows better performance. The proposed strategy is based on the idea of balancing the utilization among all machines. Experiments show that using this strategy can improve the performance of NEH significantly. Based on the above ideas, a heuristic NEH-D (NEH based on Deviation) is proposed, whose time complexity is O(mn²), the same as that of NEH. Computational results on benchmarks show that the NEH-D is significantly better than the original NEH.     

多Agent系统中信任和信誉系统研究综述

MAS环境同人类社会类似,充斥着大量不确定因素,因此,在MAS环境中引入信任来解决合作与交互问题具有重要意义.信任通常来源于直接信任和信誉两种途径,信誉系统是用于支持信任评价的机制.信誉系统的研究范围分为个体层和系统层两个层次,信誉系统的研究工作更关注个体层,信任的评价要符合准确性等特点.信誉系统中信任的表示一般采用基于认知观点和数值观点的方法.信誉系统采用集中式、分布式和混合式3种体系结构,各种模型都需要明确信任表示、传播与汇总的方法.目前两个较为成功的分布式多Agent信誉系统是ReGreT和FIRE.信息不精确的问题是信誉系统中的基本问题,也是一个迫切需要深入研究的课题,信誉系统研究中的一个突出问题是尚无公认的测试平台,Agent信誉和信任测试床(ART)项目作了有益的探索,在上述评述的基础上,可以在新模型和机制的构建、现有模型改进和完善、测试平台的研究和开发等方面有待进一步开展工作·     

RF/wireless-interconnect: The next wave of connectivity

In the era of the nanometer CMOS technology, due to stringent system requirements in power, performance and other fundamental physical limitations (such as mechanical reliability, thermal constraints, overall system form factor, etc.), future VLSI systems are relying more on ultra-high data rates (up to 100 Gbps/pin or 20 Tbps aggregate), scalable, re-configurable, highly compact and reliable interconnect fabric. To overcome such challenges, we first explore the use of multiband RF/wireless-interconnects wh...     

入侵进程的层次化在线风险评估

提出了从服务、主机和网络自下到上的层次化在线风险评估模型,实时地评估一个正在发生的入侵进程在这3个层面所产生的风险情况.在服务层面,使用了证据理论来融合报警线程中多个能够反映风险变化情况的变量来计算风险指数,通过风险指数反映入侵风险的客观情况,同时结合主观安全意识所反映出的目标风险分布,综合评估目标的风险状态.在主机层面提出了基于木桶原理的风险评估方法,在网络层面提出了安全依赖网络概念,利用了改进的风险传播算法,完成了网络层面的风险评估.提出的评估算法将报警验证、聚合与关联,以及报警置信度学习这些报警处理过程同风险评估紧密结合起来,很好地处理了风险评估中主观性、模糊性和不确定性等问题.实验表明对各种入侵进程进行的层次化在线风险评估结果与攻击实际特点相符合,为响应决策提供了有利的支持.     

Agent long-term coalition credit

Agent trust researches become more and more important because they will ensure good interactions among the software agents in large-scale open systems. Moreover, individual agents often interact with long-term coalitions such as some E-commerce web sites. So the agents should choose a coalition based on utility and trust. Unfortunately, few studies have been done on agent coalition credit and there is a need to do it in detail. To this end, a long-term coalition credit model (LCCM) is presented. Furthermore, the relationship between coalition credit and coalition payoff is also attended. LCCM consists of internal trust based on agent direct interactions and external reputation based on agent direct observation. Generalization of LCCM can be demonstrated through experiments applied in both cooperative and competitive domain environment. Experimental results show that LCCM is capable of coalition credit computation efficiently and can properly reflect various factors effect on coalition credit. Another important advantage that is a useful and basic property of credit is that LCCM can effectively filter inaccurate or lying information among interactions.     

一种求解约束优化问题的演化规划算法

提出了一种新的求解约束优化问题的演化算法--基于混合策略求解约束优化问题的演化规划算法（CMSEP）.借鉴了Mezura-Montes的算法中直接比较的约束处理方法,为求解位于边界附近的全局最优解采用多样性保护机制,允许一定比例最好不可行解进入下一代种群,混合策略变异机制用于指导算法快速搜索过程.标准测试函数的实验结果验证了算法的通用性和有效性.     

一种高功率宽带光源的研制

报道一种新型的高功率宽带光源 ,它利用 1480nm激光器对掺铒光纤进行抽运 ,经啁啾光纤光栅反射后二次放大输出 ,其功率高达 1 41mW ,线宽为 10nm。该光源工作的线性区间对应的电流为 375～ 6 45mA。     

用粗糙集理论建立决策树的一种方法

在数据挖掘中，分期是一个很重要的问题，有很多流行的分类器可以创建决策树木产生类模型。本文介绍了通过信息增益或熵的比较来构造一棵决策树的数桩挖掘算法思想，给出了用粗糙集理论构造决策树的一种方法，并用曲面造型方面的实例说明了决策树的生成过程。通过与ID3方法的比较，该种方法可以降低决策树的复杂性，优化决策树的结构，能挖掘较好的规则信息。     

存在"顿悟"的多Agent系统合作求解算法研究

在多Agent系统的研究中,多Agent合作求解一直都是研究的热点和难点.合同网(CNET)协议是通过任务共享实现有效合作的高级协议,然而现实世界的情况非常复杂,当合同网中所有Agent均无能力完成任务时,多Agent系统将陷入任务无法完成的僵局.将任务设定为问题,即产生任务等价于产生问题,完成任务等价于解决问题.写作目的是讨论与研究僵局情况的问题解决算法;并且将这种在当时环境下无法解决的、需要通过补充更多的信息和经验才可以解决的问题称为"顿悟"问题.给出了解决"顿悟"问题的3种思路及算法,并提出了对"顿悟"问题进行重构的概念和模型,对多Agent系统中"顿悟"问题的研究进行了初步的探索.