Multi-robot olfactory search in structured environments

This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots. While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for the exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.     

Top-k-size keyword search on tree structured data

Keyword search is the most popular technique for querying large tree-structured datasets, often of unknown structure, in the web. Recent keyword search approaches return lowest common ancestors (LCAs) of the keyword matches ranked with respect to their relevance to the keyword query. A major challenge of a ranking approach is the efficiency of its algorithms as the number of keywords and the size and complexity of the data increase. To face this challenge most of the known approaches restrict their ranking to a subset of the LCAs (e.g., SLCAs, ELCAs), missing relevant results.In this work, we design novel top-k-size stack-based algorithms on tree-structured data. Our algorithms implement ranking semantics for keyword queries which is based on the concept of LCA size. Similar to metric selection in information retrieval, LCA size reflects the proximity of keyword matches in the data tree. This semantics does not rank a predefined subset of LCAs and through a layered presentation of results, it demonstrates improved effectiveness compared to previous relevant approaches. To address performance challenges our algorithms exploit a lattice of the partitions of the keyword set, which empowers a linear time performance. This result is obtained without the support of auxiliary precomputed data structures. An extensive experimental study on various and large datasets confirms the theoretical analysis. The results show that, in contrast to other approaches, our algorithms scale smoothly when the size of the dataset and the number of keywords increase.     

Solution counting algorithms for constraint-centered search heuristics

Constraints have played a central role in cp because they capture key substructures of a problem and efficiently exploit them to boost inference. This paper intends to do the same thing for search, proposing constraint-centered heuristics which guide the exploration of the search space toward areas that are likely to contain a high number of solutions. We first propose new search heuristics based on solution counting information at the level of individual constraints. We then describe efficient algorithms to evaluate the number of solutions of two important families of constraints: occurrence counting constraints, such as alldifferent, and sequencing constraints, such as regular. In both cases we take advantage of existing filtering algorithms to speed up the evaluation. Experimental results on benchmark problems show the effectiveness of our approach. An earlier version of this paper appeared as [24].     

Modeling signal-based decisions in online search environments: A non-recursive forward-looking approach

Consider a rational decision maker (DM) who must acquire a finite amount of information sequentially online from a set of products. The DM receives signals on the distribution of the product characteristics. Each time an observation is acquired, DMs redefine the probability of improving upon the products observed. The resulting information acquisition process depends on the values of the characteristics observed, the number and potential realizations of the remaining observations, and the type of signal received. We construct two functions determining the information acquisition behavior of DMs and illustrate numerically the importance of the characteristic on which signals are issued.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.     

Proactive replication for rare objects in unstructured peer-to-peer networks

Unstructured peer-to-peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. The search efficiency problem in unstructured P2P networks has not been adequately addressed so far, especially concerning search for rare objects. In this paper, we propose a proactive replication strategy to improve search efficiency for rare objects. It uses an object-probing technique for peers to decide whether or not to establish replications for their objects when they join the network. This strategy can effectively increase the popularity of rare objects in order to enhance search efficiency. We also present a rare object search algorithm to reduce the overhead caused by the replication strategy. When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects. Therefore, the search request is forwarded to the peers more likely containing target objects. Simulations show that our proactive replication strategy greatly improves search efficiency for rare objects with moderate communication overhead. The rare object search algorithm not only improves search efficiency for rare objects, but also achieves load balance in search.     

二维剪切排样的束搜索启发式算法

针对约束二维矩形剪切排样问题,提出了一种基于束搜索的三阶段剪切排样算法。其切割过程包括三个阶段：板材剪切成段,段剪切成条带,条带切割成准确尺寸毛坯。采用动态规划确定段的价值,复杂度低的拼接递推不同长度子板的初始价值和板材的初始可行解,束搜索优化板材的排样方式。束搜索的节点用矩形对表示,分别是段组合而成的局部方式和未填充的剩余子板。以局部方式价值与剩余子板的初始价值之和作为节点的估计值。按估计值选择精英节点继续分支,其他节点直接删除不再回溯。实验结果表明该算法可缩短三阶段同质排样的计算时间,且所获得的余料大,利于余料的回收管理和再利用。     

A new hybrid multi-start tabu search for finding hidden purchase decision strategies in WWW based on eye-movements

It is known that the decision strategy performed by a subject is implicit in his/her external behaviors. Eye movement is one of the observable external behaviors when humans are performing decision activities. Due to the dramatic increase of e-commerce volume on WWW, it is beneficial for the companies to know where the customers focus their attention on the webpage in deciding to make a purchase. This study proposes a new hybrid multi-start tabu search (HMTS) algorithm for finding the hidden decision strategies by clustering the eye-movement data obtained during the decision activities. The HMTS uses adaptive memory and employs both multi-start and local search strategies. An empirical dataset containing 294 eye-fixation sequences and a synthetic dataset consisting of 360 sequences were experimented with. We conduct the Sign test and the result shows that the proposed HMTS method significantly outperforms its variants which implement just one strategy, and the HMTS algorithm shows an improvement over genetic algorithm, particle swarm optimization, and K-means, with a level of significance α = 0.01. The scalability and robustness of the HMTS is validated through a series of statistical tests.     

Improved Tabu search heuristics for the dynamic space allocation problem

The dynamic space allocation problem (DSAP) presented in this paper considers the task of assigning items (resources) to locations during a multi-period planning horizon such that the cost of rearranging the items is minimized. Three tabu search heuristics are presented for this problem. The first heuristic is a simple basic tabu search heuristic. The second heuristic adds diversification and intensification strategies to the first, and the third heuristic is a probabilistic tabu search heuristic. To test the performances of the heuristics, a set of test problems from the literature is used in the analysis. The results show that the tabu search heuristics are efficient techniques for solving the DSAP. More importantly, the proposed tabu search heuristic with diversification/intensification strategies found new best solutions using less computation time for one-half of all the test problems.     

Online information search and decision making: Effects of web search stance

A naturalistic online information search exposes individuals to multiple sites and conflicting perspectives. In this study, we evaluated how the holistic stance of a web search toward a product influences purchasing decisions. We recruited 109 participants who completed an initial product choice task regarding bottled water, a brief Internet search, and then a second post-search product choice task. Internet searches were analyzed to identify query terms, site visits, and stance. Results show that query terms influenced the types of sites obtained in a search, which in turn shaped the overall search stance. Participants were more likely to buy bottled water when they visited websites that emphasized environmental, economic, or health benefits for bottled water (i.e., positive stance). Participants who were asked to focus on environmental issues were less likely to buy bottled water unless packaged in recycled plastic.     

New simple and efficient heuristics for the uncapacitated single allocation hub location problem

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs.     

Local search heuristics for the mobile facility location problem

In the mobile facility location problem (MFLP), one seeks to relocate (or move) a set of existing facilities and assign clients to these facilities so that the sum of facility movement costs and the client travel costs (each to its assigned facility) is minimized. This paper studies formulations and develops local search heuristics for the MFLP. First, we develop an integer programming (IP) formulation for the MFLP by observing that for a given set of facility destinations the problem may be decomposed into two polynomially solvable subproblems. This IP formulation is quite compact in terms of the number of nonzero coefficients in the constraint matrix and the number of integer variables; and allows for the solution of large-scale MFLP instances. Using the decomposition observation, we propose two local search neighborhoods for the MFLP. We report on extensive computational tests of the new IP formulation and local search heuristics on a large range of instances. These tests demonstrate that the proposed formulation and local search heuristics significantly outperform the existing formulation and a previously developed local search heuristic for the problem.     

Using heuristic search for finding deadlocks in concurrent systems

Model checking is a formal technique for proving the correctness of a system with respect to a desired behavior. This is accomplished by checking whether a structure representing the system (typically a labeled transition system) satisfies a temporal logic formula describing the expected behavior. Model checking has a number of advantages over traditional approaches that are based on simulation and testing: it is completely automatic and when the verification fails it returns a counterexample that can be used to pinpoint the source of the error. Nevertheless, model checking techniques often fail because of the state explosion problem: transition systems grow exponentially with the number of components. The aim of this paper is to attack the state explosion problem that may arise when looking for deadlocks in concurrent systems described through the calculus of communicating systems. We propose to use heuristics-based techniques, namely the A* algorithm, both to guide the search without constructing the complete transition system, and to provide minimal counterexamples. We have realized a prototype tool to evaluate the methodology. Experiments we have conducted on processes of different size show the benefit from using our technique against building the whole state space, or applying some other methods.     

Probabilistic stopping rules for GRASP heuristics and extensions

The main drawback of most metaheuristics is the absence of effective stopping criteria. Most implementations of such algorithms stop after performing a given maximum number of iterations or a given maximum number of consecutive iterations without improvement in the best‐known solution value, or after the stabilization of the set of elite solutions found along the search. We propose effective probabilistic stopping rules for randomized metaheuristics such as GRASP (Greedy Randomized Adaptive Search Procedures). We show how the probability density function of the solution values obtained along the iterations of such algorithms can be used to implement stopping rules based on the tradeoff between solution quality and the time needed to find a solution that might improve the best solution found. We show experimentally that, in the particular case of GRASP heuristics, the solution values obtained along its iterations fit a normal distribution that may be used to give an online estimation of the number of solutions obtained in forthcoming iterations that might be at least as good as the incumbent. This estimation is used to validate the stopping rule based on the tradeoff between solution quality and the time needed to find a solution that might improve the incumbent. The robustness of this strategy is illustrated and validated by a thorough computational study reporting results obtained with GRASP implementations to four different combinatorial optimization problems.     

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.     

RTTES: Real-time search in dynamic environments

In this paper we propose a real-time search algorithm called Real-Time Target Evaluation Search (RTTES) for the problem of searching a route in grid worlds from a starting point to a static or dynamic target point in real-time. The algorithm makes use of a new effective heuristic method which utilizes environmental information to successfully find solution paths to the target in dynamic and partially observable environments. The method requires analysis of nearby obstacles to determine closed directions and estimate the goal relevance of open directions in order to identify the most beneficial move. We compared RTTES with other competing real-time search algorithms and observed a significant improvement on solution quality.     

Learning structured visual dictionary for object tracking

In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.     

基于禁忌搜索的启发式任务路径规划算法

基于启发式搜索和禁忌搜索技术,提出一种用于解决有限资源、不同重要性要求的任务路径规划问题的有效算法,通过对不同重要程度的任务进行分层调度,得到较为满意的决策结果.该算法具有搜索空间小、求解速度快的优点.仿真结果验证了算法的有效性.     

Social facilitation effect in search and decision‐making components of visual inspection

This study aimed to examine the effect of social presence on the performance of the search and decision‐making components of visual inspection. A within‐subject design experiment was conducted. Participants performed easy and difficult search and decision‐making tasks in alone and audience‐present conditions with search time, decision time, decision accuracy rates, and subjective arousal level in both components measured. Results indicated that the presence of an audience shortened (lengthened) the response time in easy (difficult) search and decision‐making components but did not influence decision accuracy rates. The social facilitation intensity in the search component was stronger than that in the decision‐making component. For both components, the performance impairment in difficult tasks was stronger than the performance improvement in easy tasks, and arousal was greater in the audience‐present condition than when working alone. The findings helped clarify the influential mechanisms of the social facilitation effect in visual inspection tasks.