Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.     

Thread-based probabilistic models for expert finding in enterprise Microblogs

Within the enterprise important conversations are shifting to Social Media Microblogs thus providing a new source for the acquisition of tacit knowledge useful for intelligent agents. While automated retrieval of candidate experts from Web documents has been well-researched, little exists that leverages social aspects of Microblogs for this purpose. We show that the experts and their related expertise can also be identified in the enterprise corpus. Analysis reveals the existence of problem-solving threads where the requestor seeking help often elicits responses from experts which consequently records tacit (experiential) knowledge. Here we present rules to automate the acquisition of this tacit knowledge. The rules are based on probabilistic models enhanced with linguistics to exploit the role patterns in threads. Heuristics also strengthen local evidence about associations between candidates, documents and (expertise) terms. Applying this, we demonstrate that the Enhanced Models can ameliorate the negative impact of Microblogs (such as sparse data, short content, and implicit associations). The experiments show that the candidate models significantly outperform the document models in the Microblog environment. This is different from previous research in Web environments. Examples also illustrate underlying insights and emphasize the ‘additive’ nature of expertise found in Social Media.     

Visual query interfaces for semantic datasets: An evaluation study

The rapid growth of the Linked Open Data cloud, as well as the increasing ability to lift relational enterprise datasets to a semantic, ontology-based level means that vast amounts of information are now available in a representation that closely matches the conceptualizations of the potential users of this information. This makes it interesting to create ontology based, user-oriented tools for searching and exploring this data. Although initial efforts were intended for tech users with knowledge of SPARQL/RDF, there are ongoing proposals designed for lay users. One of the most promising approaches is to use visual query interfaces, but more user studies are needed to assess their effectiveness. In this paper, we compare the effect on usability of two important paradigms for ontology-based query interfaces: form-based and graph-based interfaces. In order to reduce the number of variables affecting the comparison, we performed a user study with two state-of-the-art query tools developed by ourselves, sharing a large part of the code base: the graph-based tool OptiqueVQS*, and the form-based tool PepeSearch. We evaluated these tools in a formal comparison study with 15 participants searching a Linked Open Data version of the Norwegian Company Registry. Participants had to respond to 6 non-trivial search tasks using alternately OptiqueVQS* and PepeSearch. Even without previous training, retrieval performance and user confidence were very high, thus suggesting that both interface designs are effective for searching RDF datasets. Expert searchers had a clear preference for the graph-based interface, and mainstream searchers obtained better performance and confidence with the form-based interface. While a number of participants spontaneously praised the capability of the graph interface for composing complex queries, our results evidence that graph interfaces are difficult to grasp. In contrast, form interfaces are more learnable and relieve problems with disorientation for mainstream users. We have also observed positive results introducing faceted search and dynamic term suggestion in semantic search interfaces.     

多制造厂生产过程衍生废物回收处置模型构建

为了有效率地回收与处置多个制造厂在生产过程中产生的废物，针对多个制造厂与单一回收中心构成的系统，将系统中的决策者分成两种决策类型。在类型1中决策者为回收中心，提出2种回收处置模型：独立模型（模型1-1）、协同模型（模型1-2）；在类型2中决策者为各制造厂与回收中心，提出3种回收处置模型：分散模型（模型2-1）、整合模型（模型2-2）、协同模型（模型2-3）。以系统总相关成本最小化为目标构建公式，以找出最优回收处置模型，并提出一套启发式搜索算法用于协同模型中的求解。通过改变制造厂数量与相关成本参数，来观察系统成本的变化。实验结果分析表明，协同模型可使系统总成本最小，且当各制造厂之间废物量的差异程度逐渐变大时，其他回收模型较协同模型的成本差距也变大，因此协同的效果越好。     

Metaheuristics for the single machine weighted quadratic tardiness scheduling problem

This paper considers the single machine scheduling problem with weighted quadratic tardiness costs. Three metaheuristics are presented, namely iterated local search, variable greedy and steady-state genetic algorithm procedures. These address a gap in the existing literature, which includes branch-and-bound algorithms (which can provide optimal solutions for small problems only) and dispatching rules (which are efficient and capable of providing adequate solutions for even quite large instances). A simple local search procedure which incorporates problem specific information is also proposed.The computational results show that the proposed metaheuristics clearly outperform the best of the existing procedures. Also, they provide an optimal solution for all (or nearly all, in the case of the variable greedy heuristic) the smaller size problems. The metaheuristics are quite close in what regards solution quality. Nevertheless, the iterated local search method provides the best solution, though at the expense of additional computational time. The exact opposite is true for the variable greedy procedure, while the genetic algorithm is a good all-around performer.     

PRACTICAL INCONSISTENCY MANAGEMENT FOR CRITICAL-TASKS DECISION-SUPPORT SYSTEMS

Abstract

In this paper, the problem of logical consistency in critical-tasks decision systems is addressed. It is shown how local search techniques can be used to check the (in)consistency of very large propositional knowledge bases (KBs). Using new heuristic findings. we can now implement efficient forms of diagnostic reasoning in the context of very large ICBs modeling deep models of complex devices and including patterns of default reasoning.     

Metasearching

ABSTRACT

This is a selective annotated bibliography of journal articles and electronic resources relating to metasearching and metasearch tools. This bibliography is intended as a guide to the resources available to assist librarians and other information professionals interested in implementing or enhancing their metasearch projects. In addition to reviewing the major topics addressed in the publications, each annotation summarizes the important conclusions reached by the authors of the works. doi:10.1300/J136v12n03_11     

求解旅行商问题的多样化搜索帝国竞争算法

帝国竞争算法是一种局部搜索能力较强的群智能优化算法，但过度的局部搜索会导致多样性丢失并陷入局部最优。针对这一问题提出基于多样化搜索的帝国竞争算法（MSSICA）。将国家定义为一条可行解，将王国定义成四种特性不同的组合人造解方式。在搜索时使用区块机制保留各自的优势解片段，并对不同的帝国使用差异化的组合人造解方式以搜索不同解空间的有效可行解信息。在陷入局部最优时，使用多样化搜索策略注入均匀分布的可行解替换较无优势的解以提升多样性。实验结果显示，多样化搜索策略可以有效地改善帝国算法的求解多样性，并提升求解质量与稳定性。     

基于改进网格运动统计特征的图像匹配算法

为了解决尺度不变特征变换（SIFT）算法在图像匹配中匹配正确率低、耗时长等问题，提出一种基于改进网格运动统计特征RANSAC-GMS的图像匹配算法。首先，利用快速旋转不变性特征（ORB）算法对图像进行预匹配，对预匹配的特征点采用网格运动统计（GMS）来支持估计量以实现正确匹配点与错误匹配点的区分；然后，采用改进的随机抽样一致性（RANSAC）算法通过匹配点间的距离相似性对特征点进行筛选，并采用评价函数对筛选后的新数据集进行重新整理，进而实现对误匹配点的剔除。采用Oxford标准图库和现实中拍摄的图像对图像匹配算法进行测试对比，实验结果表明，所提算法在图像匹配中的平均匹配正确率达到91%以上；与GMS、SIFT、ORB等算法相比，该改进算法的近景匹配正确率和远景匹配正确率分别最少提高了16.15个百分点和3.56个百分点，说明它能有效剔除误匹配点，进一步提高图像匹配精度。     

基于Simhash的安全密文排序检索方案

针对密文检索中存在的计算量大、检索效率不高的问题，提出一种基于Simhash的安全密文排序检索方案。该方案基于Simhash的降维思想构建安全多关键词密文排序检索索引（SMRI），将文档处理成指纹和向量，利用分段指纹和加密向量构建B+树，并采用"过滤-精化"策略进行检索和排序，首先通过分段指纹的匹配进行快速检索，得到候选结果集；然后通过计算候选结果集与查询陷门的汉明距离和向量内积进行排序，带密钥的Simhash算法和安全k近邻（SkNN）算法保证了检索过程的安全性。实验结果表明，与基于向量空间模型（VSM）的方案相比，基于SMRI的排序检索方案计算量小，能节约时间和空间成本，检索效率高，适用于海量加密数据的快速安全检索。