Search results for "Search procedure"

showing 4 items of 24 documents

Randomized heuristics for the Capacitated Clustering Problem

2017

In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-less approach that constructs independent solutions, while the latter is a memory-based method that constructs linked solutions, obtained by partially rebuilding previous ones. Both are based on the combination of greediness and randomization in the constructive process, and coupled with a subsequent l…

MatheuristicMathematical optimizationInformation Systems and Management0211 other engineering and technologies02 engineering and technologyCapacitated ClusteringTheoretical Computer ScienceArtificial Intelligence0202 electrical engineering electronic engineering information engineeringLocal search (optimization)Cluster analysisGreedy randomized adaptive search procedureMathematicsGrasp021103 operations researchbusiness.industryHeuristicGRASPGraph partitioningGraph partitionComputer Science ApplicationsControl and Systems EngineeringSimulated annealing020201 artificial intelligence & image processingHeuristicsbusinessSoftware
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Optimization of Data Harvesters Deployment in an Urban Areas for an Emergency Scenario

2013

International audience; Since its appearance in the VANETs research community, data collection where vehicles have to explore an area and collect various local data, brings various issues and challenges. Some architectures were proposed to meet data collection requirements. They can be classified into two categories: Decentralized and Centralized self-organizing where different components and techniques are used depending on the application type. In this paper, we treat time-constrained applications in the context of search and rescue missions. For this reason, we propose a centralized architecture where a central unit plans and manages a set of vehicles namely harvesters to get a clear ove…

OptimizationMathematical optimizationVANETOperations researchComputer scienceHeuristic (computer science)[SPI] Engineering Sciences [physics]Search and Rescue050801 communication & media studies02 engineering and technologyTopology[SPI]Engineering Sciences [physics]0508 media and communications11. Sustainability0202 electrical engineering electronic engineering information engineeringHeuristic algorithmsLocal search (optimization)Greedy algorithmMetaheuristicHarvestersGreedy randomized adaptive search procedureIncremental heuristic searchbusiness.industryData Collection05 social sciencesVehicles020206 networking & telecommunicationsRoadsEmergencyBeam searchbusinessBismuthVariable neighborhood search
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A Maximal-Space Algorithm for the Container Loading Problem

2008

In this paper, a greedy randomized adaptive search procedure (GRASP) for the container loading problem is presented. This approach is based on a constructive block heuristic that builds upon the concept of maximal space, a nondisjoint representation of the free space in a container. This new algorithm is extensively tested over the complete set of Bischoff and Ratcliff problems [Bischoff, E. E., M. S. W. Ratcliff. 1995. Issues in the development of approaches to container loading. Omega 23 377–390], ranging from weakly heterogeneous to strongly heterogeneous cargo, and outperforms all the known nonparallel approaches that, partially or completely, have used this set of test problems. When …

Set (abstract data type)Mathematical optimizationHeuristic (computer science)Computer scienceContainer (abstract data type)GRASPGeneral EngineeringParallel algorithmAlgorithm designAlgorithmGreedy randomized adaptive search procedureBlock (data storage)INFORMS Journal on Computing
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A Serendipity-Oriented Greedy Algorithm for Recommendations

2017

Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most …

ta113SerendipityComputer sciencebusiness.industrysuosittelujärjestelmät020207 software engineeringserendipity02 engineering and technologyalgorithmsunexpectednessnoveltyalgoritmit0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencerecommender systemsGreedy algorithmbusinessGreedy randomized adaptive search procedure
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