基于语义搜索的实名呼叫系统设计

网络和通信技术的飞速发展,使传统的通信方式已经不能满足人们的需求。通信的人性化和智能化成为各个通信厂商追求的目标,但却始终无法摆脱号码这一枷锁。本文针对只能通过号码呼叫带来的通信不便问题,提出利用语义搜索这一未来互联网搜索技术实现实名呼叫的设计方案,并将语音搜索的思想融入其中,使通信变得更简单,实现通信的以人为本。     

基于可信度的Web信息搜索主动服务研究

现有的Web信息搜索方式是基于关键词匹配来进行的,其准确性和可靠性有限。本文以用户需求为中心,收集用户偏好,应用后台软件采用C4.5决策树算法构建文件过滤规则,结合基于主观Bayes方法的不确定性推理为过滤规则追加可信度支持,并用模糊规则来描述,确保Web信息搜索的查全率和查准率全面提升。     

基于改进的参考矢量选取的码字快速搜索算法

矢量量化的编码过程计算复杂性极高,为了减少编码时大量的矢量间距离计算,许多文献提出利用不等式关系以较少的计算量来估算距离的方法。在Chang等人提出的利用双限制三角不等式的快速搜索算法基础上,通过改进参考矢量的选取方法,有效提高了码字搜索的效率。实验结果表明,改进算法的码字排除率可以提高3.735%9.976%,编码时间可以减少6.03%35.25%。     

启发式A^＊算法在游戏寻路中的应用

文章在对A＊搜索和启发式搜索技术进行详细分析与研究的基础上,将A＊算法应用于flash游戏中的寻路,并在此基础上分析出A＊算法的实际应用时间效率和空间效率。     

基于多Agent系统的自调节及协同工作的组合投资模型

Agent技术特别是多Agent系统（MAS,Multi-Agent System）为解决人工智能等领域复杂问题提供了一个新途径,多Agent系统重点研究如何协调系统中的各个Agent的行为使其协同工作。针对多阶段组合投资问题,提出了一个基于多Agent系统的自调节及协同工作的组合投资策略模型。该模型系统中的各个Agent通过通讯共享知识,在求解问题的搜索空间中进行协同搜索,在更短的搜索步长内得到问题的解,极大地提高了系统性能。该模型具有不基于任何股票模型、时间复杂度低以及逼近最优投资策略速度较快等优点,实验证明具有一定的实际意义。     

P2P资源智能查询策略的研究与设计

提高资源搜索效率、提高网络的扩展性一直是P2P网络的关键技术问题。在分析现有的P2P网络资源搜索的算法及其存在的问题的基础上,作者提出并设计了一种基于最近Query消息查询记录表、本地资源推荐路由表相结合的智能查询策略。能在保持原有搜索覆盖率的前提下,提高网络资源搜索效率,减少Query冗余信息,提高了网络的扩展性。     

基于模式搜索的连续函数寻优

模式搜索方法是求解最优化问题的一种直接搜索方法,它只根据函数值信息来进行求优。但模式搜索对起始点非常敏感,对好的起始点,模式搜索可以搜索到全局最优点,但对某些起始点,模式搜索只能搜索到局部极值点。本文提出基于模式搜索的一个新方法,实验证明,它能搜索到全局最优。     

基于蚂蚁算法的制造资源搜索调度研究

本文基于对制造资源调度和发现过程的分析研究,建立实用的制造资源调度器模型和调度策略,然后采用蚂蚁算法对制造资源的调度快表做出优化,并做出有关的实验和数据分析。该方法能够有效地实现制造资源的作业的合理调度和实现分布式系统的负载平衡,提高了制造资源搜索的成功率。     

Multi-strategy ensemble particle swarm optimization for dynamic optimization

Optimization in dynamic environments is important in real-world applications, which requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. To tackle the challenges of dynamic optimization, several strategies have been proposed to enhance the performance of PSO, and have gained success on various dynamic optimization problems. But there still exist some issues in dynamic optimization which need to be studied carefully, i.e. the robustness of the algorithm to problems of various dynamic features. In this paper, a new multi-strategy ensemble particle swarm optimization (MEPSO) for dynamic optimization is proposed. In MEPSO, all particles are divided into two parts, denoted as part I and part II, respectively. Two new strategies, Gaussian local search and differential mutation, are introduced into these two parts, respectively. Experimental analyses reveal that the mechanisms used in part I can enhance the convergence ability of the algorithm, while mechanisms used in part II can extend the searching area of the particle population to avoid being trapped into the local optimum, and can enhance the ability of catching up with the changing optimum in dynamic environments. The whole algorithm has few parameters that need to be tuned, and all of them are not sensitive to problems. We compared MEPSO with other PSOs, including MQSO, PHPSO and Standard PSO with re-initialization, on moving peaks Benchmark and dynamic Rastrigin function. The experimental results show that MEPSO has pretty good performance on almost all testing problems adopted in this paper, and outperforms other algorithms when the dynamic environment is unimodal and changes severely, or has a great number of local optima as dynamic Rastrigin function does.     

A multiperiod set covering location model for dynamic redeployment of ambulances

Emergency medical service (EMS) providers continually seek ways to improve system performance particularly the response time to incidents. The demand for ambulances fluctuate throughout the week, depending on the day of week, and even the time of day, therefore EMS operators can improve system performance by dynamic relocation/redeployment of ambulances in response to fluctuating demand patters. The objective of the model is to determine the minimum number of ambulances and their locations for each time cluster in which significant changes in demand pattern occur while meeting coverage requirement with a predetermined reliability. The model is further enhanced by calculating ambulance specific busy probabilities and validated by a comprehensive simulation model. Computational results on experimental data sets and data from an EMS agency are provided.