Inferior search engine’s optimal choice: Knowledge-sharing service versus search quality

Google has been steadily increasing its market share in the US, although its main competitor, Yahoo, began developing a successful knowledge-sharing service in 2005. To verify whether a knowledge-sharing service may increase a search engine’s competitiveness, this study considers the competition between an inferior search engine that has an option of introducing a knowledge-sharing service and a superior search engine without this service. We specifically investigate the conditions under which it would be more profitable for the inferior search engine to introduce a knowledge-sharing service rather than increase its search quality. We show that the inferior search engine’s profit-maximizing strategy mainly depends on both the amount of information available on the Internet and the difference in search quality between it and the superior search engine. When the search quality difference is small, the inferior search engine should introduce a knowledge-sharing service keeping its answer database inaccessible to the superior search engine. When the search quality difference is large, the inferior search engine generally had better improve its search technology. We also show the inferior search engine’s market-share-maximizing strategy when it introduces a knowledge-sharing service.     

用户搜索体验质量及搜索结果排序

搜索引擎的索引库内容更新存在一定周期,目前在提供Web页面搜索服务时,搜索结果的排序没有考虑其索引资源的实时质量特征,难以满足用户搜索的多层次要求。提出了用户搜索体验质量模型,并把该模型应用于开源搜索引擎Nutch中。在提供搜索服务时,Nutch结合影响搜索体验质量的因素实现搜索结果的排序。实验给出了用户搜索体验质量变化情况,反应了这些因素对于搜索结果排序及用户搜索体验质量的重要性。该实验为下一步提升用户搜索体验质量,减少搜索引擎及网络传输能耗,进而实现搜索服务整体优化工作奠定了基础。     

黄页搜索引擎中二次检索的实现技术

搜索引擎的目的在于快速有效地找到用户需求的信息,在黄页搜索引擎中,通过关键字搜索,可以返回大量的信息,但其中的大部分信息不在用户需求的范围内.二次检索的定义是,在一次检索结果的基础上,按照特定的条件进行筛选,获得更为满足用户需求的结果.显然,二次检索在黄页搜索中的应用是必要的.基于这样的需求,提出了一种使用内存数据库和黄页搜索引擎联合进行二次搜索的方法.     

Display Movement Velocity and Dynamic Visual Search Performance

In practice, many visual search tasks are performed under dynamic conditions. An experiment was conducted here to test visual search strategy adopted by a person in a dynamic visual search and to investigate the effects of display movement velocity on search time and detection accuracy in it. Thirty‐five participants were randomly tested with all 10 angular velocities of 0, 2, 4, 6, 8, 10, 12, 14, 16, and 32 deg/s. The data obtained fitted the random search model well. The results revealed that observers utilized a random search strategy during the dynamic visual search process and that display movement velocity influenced search performance. In comparison with static visual search, an angular velocity faster than 4 deg/s resulted in a significant decrement in search performance. The variations of duration of individual fixations, the probability of target detection in a single fixation and visual lobe area were discussed. The obtained relationships between display movement velocity, search time and detection accuracy can serve as a useful guide for designing a dynamic search task, thus helping to maximize the cost‐effectiveness of dynamic search tasks while minimizing errors and misses during the search process.     

A spy search mechanism for memetic algorithm in dynamic environments

Searching within the sample space for optimal solutions is an important part in solving optimization problems. The motivation of this work is that today’s problem environments have increasingly become dynamic with non-stationary optima and in order to improve optima search, memetic algorithm has become a preferred search method because it combines global and local search methods to obtain good solutions. The challenge is that existing search methods perform the search during the iterations without being guided by solid information about the nature of the search environment which affects the quality of a search outcome. In this paper, a spy search mechanism is proposed for memetic algorithm in dynamic environments. The method uses a spy individual to scope out the search environment and collect information for guiding the search. The method combines hyper-mutation, random immigrants, hill climbing local search, crowding and fitness, and steepest mutation with greedy crossover hill climbing to enhance the efficiency of the search. The proposed method is tested on dynamic problems and comparisons with other methods indicate a better performance by the proposed method.     

Multi-goal economic search using dynamic search structures

This paper investigates cooperative search strategies for agents engaged in costly search in a complex environment. Searching cooperatively, several search goals can be satisfied within a single search effort. Given the searchers’ preferences, the goal is to conduct a search in a way that the expected overall utility out of the set of opportunities found (e.g., products when operating in a market) minus the costs associated with finding that set is maximized. This search scheme, given in the context of a group search, applies also to scenarios where a single agent has to search for a set of items for satisfying several different goals. The uniqueness of the proposed mechanism is in the ability to partition the group of agents/goals into sub-groups where the search continues for each group autonomously. As we show throughout the paper, this strategy is favorable as it weakly dominates (i.e., can improve but never worsen) cooperative and autonomous search techniques. The paper presents a comprehensive analysis of the new search method and highlights the specific characteristics of the optimal search strategy. Furthermore, we introduce innovative algorithms for extracting the optimal search strategy in a range of common environments, that eliminates the computational overhead associated with the use of the partitioning technique.     

Novel approaches to efficient flooding search in peer-to-peer networks

Efficient search algorithms are crucial to the success of unstructured and hybrid peer-to-peer networks. Performance requirements on search algorithms include low search traffic, low search latency, and determinism in returning the searched items. However, existing search algorithms fail to meet these goals. We propose, analyze, and evaluate two novel flooding search algorithms. The first algorithm conducts on-the-fly estimation of the popularity of the searched item, and uses such knowledge to guide a peer’s search process. It requires the minimum search cost and very low latency, and albeit its non-determinism, often returns the desired number of results. The second algorithm, Hurricane flooding, exponentially expands the search horizon of the source of a search in a spiral pattern. Hurricane flooding is deterministic, requires search cost arbitrarily close to a lower bound, and returns the results in logarithmic time. We analyze and optimize our proposed algorithms, and evaluate them using various network models, including a real Gnutella network topology.     

毫米波通信中一种优化的波束搜索算法研究

波束形成技术成为毫米波无线通信的关键技术之一,其中码本搜索算法成为研究重点.IEEE802.15.3c及IEEE802.1 1ad中提出的均是基于遍历搜索的算法,这类算法随着码本数目的增加,搜索时间将变的难以接受.本文提出一种最优扇区数目波束搜索算法,该算法根据波束级码本数目,求出搜索次数最少的扇区数目,并构造相应的扇区码本,然后先在构造的扇区码本上进行搜索,再进行波束搜索.最优扇区数目波束搜索算法利用最优的扇区数目大大减小的波束搜索复杂度,减小的波束搜素所需时间.理论分析和程序仿均证明了最优扇区数目波束搜索算法的在减少搜索次数方面的有效性.     

Randomised restarted search in ILP

Recent statistical performance studies of search algorithms in difficult combinatorial problems have demonstrated the benefits of randomising and restarting the search procedure. Specifically, it has been found that if the search cost distribution of the non-restarted randomised search exhibits a slower-than-exponential decay (that is, a “heavy tail”), restarts can reduce the search cost expectation. We report on an empirical study of randomised restarted search in ILP. Our experiments conducted on a high-performance distributed computing platform provide an extensive statistical performance sample of five search algorithms operating on two principally different classes of ILP problems, one represented by an artificially generated graph problem and the other by three traditional classification benchmarks (mutagenicity, carcinogenicity, finite element mesh design). The sample allows us to (1) estimate the conditional expected value of the search cost (measured by the total number of clauses explored) given the minimum clause score required and a “cutoff” value (the number of clauses examined before the search is restarted), (2) estimate the conditional expected clause score given the cutoff value and the invested search cost, and (3) compare the performance of randomised restarted search strategies to a deterministic non-restarted search. Our findings indicate striking similarities across the five search algorithms and the four domains, in terms of the basic trends of both the statistics (1) and (2). Also, we observe that the cutoff value is critical for the performance of the search algorithm, and using its optimal value in a randomised restarted search may decrease the mean search cost (by several orders of magnitude) or increase the mean achieved score significantly with respect to that obtained with a deterministic non-restarted search. Editors: Rui Camacho     

基于BDD的增量启发式搜索

增量搜索是一种利用先前的搜索信息提高本次搜索效率的方法,通常可以用来解决动态环境下的重规划问题.在人工智能领域,一些实时系统常常需要根据外界环境的变化不断修正自身,这样就会产生一系列变化较小的相似问题,此时应用增量搜索将会非常有效.另外,基于BDD(binary decision diagram)的启发式搜索,结合了基于BDD的搜索和启发式搜索这两种方法的优点.它既用BDD这一紧凑的数据结构来表示系统的状态空间,又通过使用启发信息来进一步压缩搜索树的大小.在介绍基于BDD的启发式搜索和增量搜索之后,结合这两种方法给出了基于BDD的增量启发式搜索算法--BDDRPA*.大量的实验结果表明,BDDRPA*算法是非常有效的,它可以被广泛地应用到智能规划、移动机器人问题等领域中.     

改进的二分法查找

当前有很多的查找算法,其中在对有序数列的查找算法中二分法查找（binary search）是最常用的。利用二分法,在含有n个元素的有序数列中查找一个元素的最大比较次数为[logn]＋1。在很多情况中,在查找之前有序数列分布的很多信息为已知,比如说如果知道了有序数列中每相邻两个元素之差的最大值的一个上界,就可以有比二分法更加有效的查找算法。文章给出了一个称之为改进的二分法查找算法。改进的二分法查找性能明显优于二分法查找,受数列分布的影响,其最坏情况下查找一个元素的最大比较次数在1和[logn]＋1之间,明显优于二分查找的[logn]＋1。在实际应用中利用改进的二分法可以极大地提高查找效率。     

Spherical evolution for solving continuous optimization problems

In these years, more and more nature-inspired meta-heuristic algorithms have been proposed; search operators have been their core problem. The common characteristics or mechanism of search operators in different algorithms have not been represented by a standard format. In this paper, we first propose the concept of a search pattern and a search style represented by a mathematical model. Second, we propose a new search style, namely a spherical search style, inspired by the traditional hypercube search style. Furthermore, a spherical evolution algorithm is proposed based on the search pattern and spherical search style. At the end, 30 benchmark functions of CEC2017 and a real-world optimization problem are tested. Experimental results and analysis demonstrate that the proposed method consistently outperforms other state-of-the-art algorithms.     

Search Strategies for Multiple UAV Search and Destroy Missions

Multiple UAVs are deployed to carry out a search and destroy mission in a bounded region. The UAVs have limited sensor range and can carry limited resources which reduce with use. The UAVs perform a search task to detect targets. When a target is detected which requires different type and quantities of resources to completely destroy, then a team of UAVs called as a coalition is formed to attack the target. The coalition members have to modify their route to attack the target, in the process, the search task is affected, as search and destroy tasks are coupled. The performance of the mission is a function of the search and the task allocation strategies. Therefore, for a given task allocation strategy, we need to devise search strategies that are efficient. In this paper, we propose three different search strategies namely; random search strategy, lanes based search strategy and grid based search strategy and analyze their performance through Monte-Carlo simulations. The results show that the grid based search strategy performs the best but with high information overhead.     

Multi-direction search algorithm for block motion estimation in H.264/AVC

Many efficient search algorithms such as three-step search, new three-step search, four-step search, block-based gradient descent search, diamond search and hexagon-based search are developed for block motion estimation (ME) to search for the optimal objective function. The block ME technique involves an optimisation problem. Although these algorithms can converge to a minimal point rapidly, they suffer from becoming trapped in local minimum if the objective function has multiple minima. To solve this problem, the hybrid multi-hexagongrid search (UMHexagonS) algorithm has been proposed in H.264/AVC, in which an unsymmetrical-cross search and an uneven UMHexagonS are employed over a wide search range to find a nearly global minimum. The experiment shows that the hybrid UMHexagonS algorithm is computation expensive and is occasionally trapped in local minimum. The authors propose a novel and fast search algorithm, called multi-direction search (MDS) algorithm, which uses an MDS first to find all possible locally optimal points and then uses the extended hexagon search to refine these points for the final optimal motion vector. The experimental results indicate that a significant improvement in computation reduction (~30 and 50% reduction in average search points, corresponding to 19 and 37% reduction in total encoding time, for MDS and fast MDS, respectively) can be achieved while maintaining better coding performance, compared with the hybrid UMHexagonS algorithm.     

Consumer web search behaviour: diagrammatic illustration of wayfinding on the web

External information search behaviour has long been of interest to consumer researchers. Experimental and post hoc survey research methodologies have typically used a large number of variables to record search activity. However, as these are usually considered in aggregate, there is little opportunity for the researcher to overview the search style of a consumer. To date, the diagrammatic illustration of search behaviour has been limited to experimental environments in which the available information was strictly bounded, for example, within databases or when information display boards have been used. This paper, which focuses largely on inter-site world wide web (WWW) search behaviour, discusses web search paradigms and the variables used to capture WWW search. It also provides a conceptual framework for the representation of external information search behaviour in diagrammatic form. The technique offers researchers an opportunity to holistically interpret information search data and search styles. The benefits include the identification of particular search styles, more precise interpretation of web search activity numeric data and the potential application for the training of web users to improve their search effectiveness.     

Internet搜索引擎精确检索算法及实现

Internet网络环境搜索引擎主要由三部分组成:搜索器、索引数据库和用户界面。检索器是搜索引擎中的核心和关键。通常的网络搜索引擎采用的是集合或模糊检索算法,其检索结果冗余大。主要介绍了搜索引擎索引数据库的结构;基于Spider的通用搜索器的实现;索引表的生成过程;精确检索原理、算法及实现。采用精确检索算法的搜索引擎,所搜索的信息冗余度小并且效率高。     

A hybrid scatter search meta-heuristic for delay-constrained multicast routing problems

This paper investigates the first hybrid scatter search and path relinking meta-heuristic for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. The underpinning mathematic model of the DCLC multicast routing problem is the constrained Steiner tree problem in graphs, a well known NP-complete problem. After combining a path relinking method as the solution combination method in scatter search, we further explore two improvement strategies: tabu search and variable neighborhood search, to intensify the search in the hybrid scatter search algorithm. A large number of simulations on some benchmark instances from the OR-library and a group of random graphs of different characteristics demonstrate that the improvement strategy greatly affects the performance of the proposed scatter search algorithm. The hybrid scatter search algorithm intensified by a variable neighborhood descent search is highly efficient in solving the DCLC multicast routing problem in comparison with other algorithms and heuristics in the literature.     

The pathology of heuristic search in the 8-puzzle

In practice, an incomplete heuristic search nearly always finds better solutions if it is allowed to search deeper, i.e. expand and heuristically evaluate more nodes in the search tree. On the rare occasions when searching deeper is not beneficial, a curious phenomenon called ‘search pathology’ occurs. In this article, we study the pathology and gain of a deeper search of the minimin algorithm in the 8-puzzle, a domain often used for evaluating single-agent search algorithms. We have analysed the influence of various properties of the search tree and the heuristic evaluation function on the gain and the pathology. In order to investigate a broad range of the properties, the original 8-puzzle was extended with diagonal moves, yielding a larger variety of search trees. It turned out that in the 8-puzzle, a substantial proportion of the solvable positions is pathological under various parameters. More importantly, the search parameters that enable the highest gains are quite consistent in pathological and non-pathological positions alike, thus pointing to potentially successful search strategies.     

个性化元搜索关键技术的研究

元搜索引擎是搜索引擎之上的搜索引擎,它主要通过成员搜索引擎调度、文本选择、结果整合三个主要步骤来为用户提供更加全面的信息。本文通过对元搜索引擎关键技术的研究,在成员调度、结果整合时考虑用户兴趣,使得选择的成员搜索引擎、搜索结果更加符合用户的需求,从而实现元搜索的个性化,在一定程度上提高了元搜索引擎的查准率和效率。