共查询到20条相似文献,搜索用时 125 毫秒
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在克隆选择算法搜索函数最优解问题的研究中,针对传统自适应动态克隆选择算法收敛速度慢、精度低以及种群多样性低的缺点,提出了一个基于球面杂交的自适应动态克隆选择算法。新算法采用浮点数编码方式,在每次迭代过程中,首先根据抗体的亲和度动态计算出每个抗体的变异概率,然后根据亲和度大小将抗体种群动态分为记忆单元和一般抗体单元,并采用球面杂交方式对种群进行调整,提高了算法的收敛速度和求解精度。实例验证了所提算法的有效性和可行性。 相似文献
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针对传统克隆选择算法的不足,提出了一个基于球面杂交的新型克隆选择算法。在该算法的每次迭代过程中,动态地计算出每个抗体的变异概率,根据抗体的亲和度将抗体种群动态分为记忆单元和一般抗体单元,并以球面杂交方式对种群进行调整,从而加快了算法的全局搜索速度。实例验证了所提算法的有效性、可行性。 相似文献
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为了克服传统免疫克隆选择算法的种群缺乏多样性、抗体选择不具随机性的缺点,提出了一种新型动态自适应免疫克隆选择算法。在该算法求解过程中,根据抗体的亲和度将抗体种群动态地分为记忆单元和一般抗体单元,以球面杂交方式对种群进行调整并动态修正每个抗体的变异概率,从而保障了群体多样性,加快了算法的全局搜索速度。实例验证了所提算法具有较好的性能。 相似文献
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在优化克隆算法的研究中,针对传统的克隆选择算法存在收敛性差和局部最优问题,提出一种多记忆抗体克隆选择原理的人工免疫网络算法。在克隆选择算法的基础上通过引入替代阀值因子,利用随机生成的新抗体组成种群替代原种群中对抗原亲和力最小抗体,同时增设变异概率的概念,达到在一定程度上避免记忆抗体种群的退化现象,提高算法的全局优化能力,避免陷入局部最优。仿真结果表明,算法加快了种群亲和力成熟的进程,随着进化代数的增加检测率总体呈上升趋势,能更好的应用于大规模各种识别问题中。 相似文献
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基于自适应克隆启发算法的作业车间调度 总被引:2,自引:1,他引:1
将优先权启发式算法获得的最小生产周期倒数作为抗体,采用实数编码,给出新的自适应克隆启发算法,用于求解作业车间调度问题。设计一种新的自适应克隆算子,基于抗体间距离的大小,在抗体间自适应地分配抗体激励度和抗体克隆的数量,模拟免疫系统自适应地调节抗体克隆数量的动态过程。FT10和FT06的仿真实验表明,该算法性能稳定、效果良好。 相似文献
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针对免疫算法在全局优化过程中多样性不足的问题,将差异进化引入克隆变异操作中,提出了一个新的改进的克隆选择算法——基于差异进化的克隆选择算法(DECSA),算法将差异进化和克隆超变异相结合,促进了抗体与抗体之间的信息融合,使得子代抗体继承父代抗体的信息的同时,携带着不同父代个体信息,丰富了抗体种群的多样性,实现了在同一父代抗体周围的多个方向同时进行全局和局部搜索。对13个标准测试函数的测试结果及与已有的算法的比较表明,该算法表现出较好的局部搜索和全局搜索能力。 相似文献
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Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data 总被引:2,自引:0,他引:2
Xiang Lian Lei Chen 《The VLDB Journal The International Journal on Very Large Data Bases》2009,18(3):787-808
Reverse nearest neighbor (RNN) search is very crucial in many real applications. In particular, given a database and a query
object, an RNN query retrieves all the data objects in the database that have the query object as their nearest neighbors.
Often, due to limitation of measurement devices, environmental disturbance, or characteristics of applications (for example,
monitoring moving objects), data obtained from the real world are uncertain (imprecise). Therefore, previous approaches proposed
for answering an RNN query over exact (precise) database cannot be directly applied to the uncertain scenario. In this paper,
we re-define the RNN query in the context of uncertain databases, namely probabilistic reverse nearest neighbor (PRNN) query,
which obtains data objects with probabilities of being RNNs greater than or equal to a user-specified threshold. Since the
retrieval of a PRNN query requires accessing all the objects in the database, which is quite costly, we also propose an effective
pruning method, called geometric pruning (GP), that significantly reduces the PRNN search space yet without introducing any
false dismissals. Furthermore, we present an efficient PRNN query procedure that seamlessly integrates our pruning method.
Extensive experiments have demonstrated the efficiency and effectiveness of our proposed GP-based PRNN query processing approach,
under various experimental settings. 相似文献
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组最近邻查询是空间对象查询领域的一类重要查询,通过该查询可找到距离给定查询点集最近的空间对象.由于图像分辨率或解析度的限制等因素,空间对象的存在不确定性广泛存在于某些涉及图像处理的查询应用中.这些对象位置数据的存在不确定性会对组最近邻查询结果产生影响.本文给出面向存在不确定对象的概率阈值组最近邻查询定义,设计了高效的查询处理机制,通过剪枝优化等手段提高概率阈值组最近邻查询效率,并进一步提出了高效概率阈值组最近邻查询算法.采用多个真实数据集对概率阈值组最近邻算法进行了实验验证,结果表明所提算法具有良好的查询效率. 相似文献
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一种局部相关不确定数据库快照集合上的概率频繁最近邻算法 总被引:2,自引:0,他引:2
局部相关空间不确定数据越来越受到许多实际应用的关注.提出了一种新颖的定义在不确定数据库的多个快照上的概率频繁近邻查询,目的是在多个快照数据上找到以一定概率频繁成为查询点最近邻的那些对象.应用现有的基于传统数据和基于不确定数据上的近邻查询算法直接处理这种查询会产生昂贵的开销.为了很好地解决这一问题,提出了一般的处理框架,... 相似文献
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Xiang Lian Lei Chen 《Knowledge and Data Engineering, IEEE Transactions on》2008,20(6):809-824
The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, top-k query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. Specifically, given a set, Q, of query points, a PGNN query retrieves data objects that minimize the aggregate distance (e.g., sum, min, and max) to query set Q. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach, in terms of the wall clock time and the speed-up ratio against linear scan. 相似文献
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Daichi Amagata Yuya Sasaki Takahiro Hara Shojiro Nishio 《Distributed and Parallel Databases》2016,34(2):259-287
A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost. 相似文献
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反向最近邻查询是空间数据库空间查询的研究热点。目前反向最近邻查询的查询粒度都是基于一维的点.在一些空间物体不能抽象为点的情况下将其抽象为点进行反向最近邻查询,查询结果不能达到一定的精度。该文在分析基于平面线段的最近邻查询和R树结构的基础上提出了一种改进的R树-Rcd树,并给出了基于Rcd树的平面线段反向最近邻查询算法.该方法能实现平面线段的反向最近邻查询。 相似文献
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反向最近邻查询是空间数据库空间查询的研究热点。目前反向最近邻查询的查询粒度都是基于一维的点,在一些空间物体不能抽象为点的情况下将其抽象为点进行反向最近邻查询,查询结果不能达到一定的精度。该文在分析基于平面线段的最近邻查询和R树结构的基础上提出了一种改进的R树—Rcd树,并给出了基于Rcd树的平面线段反向最近邻查询算法,该方法能实现平面线段的反向最近邻查询。 相似文献