Search results for "Heuristic"
showing 10 items of 476 documents
Object Clustering Methods and a Query Decomposition Strategy for Distributed Object-Based Information Systems
1999
Emerging developments and advances in distributed processing have created a need for tools and methods to partition and distribute information systems across interconnected processors. In particular, distribution approaches which take into account the key characteristics of OO concepts are required to extend traditional fragmentation results to object oriented database systems. To fulfill the above requirements, we propose a methodology for the distribution design of object-based information systems. The underlying approach consists of techniques and heuristics that can be used to create clusters of inter-related object classes that can be fragmented interdependently, producing distribution…
Tabu search with strategic oscillation for the quadratic minimum spanning tree
2014
The quadratic minimum spanning tree problem consists of determining a spanning tree that minimizes the sum of costs of the edges and pairs of edges in the tree. Many algorithms and methods have been proposed for this hard combinatorial problem, including several highly sophisticated metaheuristics. This article presents a simple Tabu Search (TS) for this problem that incorporates Strategic Oscillation (SO) by alternating between constructive and destructive phases. The commonalties shared by this strategy and the more recently introduced methodology called iterated greedy search are shown and implications of their differences regarding the use of memory structures are identified. Extensive …
Guided local search for the optimal communication spanning tree problem
2011
This paper considers the optimal communication spanning tree (OCST) problem. Previous work analyzed features of high-quality solutions. Consequently, integrating this knowledge into a metaheuristic increases its performance for the OCST problem. In this paper, we present a guided local search (GLS) approach which dynamically changes the objective function to guide the search process into promising areas. In contrast to traditional approaches which reward promising solution features by favoring edges with low weights pointing towards the tree's center, GLS penalizes low-quality edges with large weights that do not point towards the tree's center.
Optimal Usage of Multiple Network Connections
2008
In the future mobile networks, a mobile terminal is able to select the best suitable network for each data transmission. The selection of a network connection to be used has been under a lot of study. In this paper, we consider a more extensive case in which we do not select a network connection but use several network connections simultaneously to transfer data. When data is transferred using multiple network connections, a network connection has to be selected for each component of the data. We have modelled this problem as a multiobjective optimization problem and developed a heuristic to solve the problem fast in a static network environment. In this paper, we discuss solving the proble…
Algorithms for the Maximum Weight Connected $$k$$-Induced Subgraph Problem
2014
Finding differentially regulated subgraphs in a biochemical network is an important problem in bioinformatics. We present a new model for finding such subgraphs which takes the polarity of the edges (activating or inhibiting) into account, leading to the problem of finding a connected subgraph induced by \(k\) vertices with maximum weight. We present several algorithms for this problem, including dynamic programming on tree decompositions and integer linear programming. We compare the strength of our integer linear program to previous formulations of the \(k\)-cardinality tree problem. Finally, we compare the performance of the algorithms and the quality of the results to a previous approac…
On the Non-Intrusive Load Monitoring in dwellings: a feasibility perspective
2021
The oncoming modernization process of the power grids, driven above all by decarbonisation objectives and the continuous improvement of digital technologies, is encouraging active participation in the electricity market by consumers through the Demand-Response mechanism. From this perspective, the introduction of smart meters and energy consumption monitoring devices plays a fundamental role, being able to give benefits to consumers, suppliers and the electricity grid itself. This paper proposes a supervised method of non-intrusive load monitoring (NILM) based on the recognition of patterns in the time domain with the Dynamic Time Warping algorithm which is suitable for low-cost smart meter…
An Online Time Warping based Map Matching for Vulnerable Road Users’ Safety
2018
International audience; High penetration rate of Smartphones and their increased capabilities to sense, compute, store and communicate have made the devices vital components of intelligent transportation systems. However, their GPS positions accuracy remains insufficient for a lot of location-based applications especially traffic safety ones. In this paper, we developed a new algorithm which is able to improve smartphones GPS accuracy for vulnerable road users' traffic safety. It is a two-stage algorithm: in the first stage GPS readings obtained from smartphones are passed through Kalman filter to smooth deviated reading. Then an adaptive online time warping based map matching is applied to…
Information and Investor Behavior Surrounding Earnings Announcements
2014
Preliminary version The goal of this paper is to analyze the impact of annual earnings announcements on the market through the order flow data in addition to the usual transaction data. In this respect, examining order flow data can potentially reveal valuable information which is not available from transaction data. In fact, the data allow us to test hypotheses about asymmetric information and investor behavior and to test if the behavior varies with investor sophistication. In addition, the paper tries to identify the determinants of the impact on a firm's value using assumptions about investor behavior.
A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data
2021
The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that…
SELECTING HERB-RICH FOREST NETWORKS TO PROTECT DIFFERENT MEASURES OF BIODIVERSITY
2001
Data on vascular plants of herb-rich forests in Finland were used to compare the efficiency of reserve selection methods in representing three measures of biodiversity: species richness, phylogenetic diversity, and restricted-range diversity. Comparisons of reserve selection methods were carried out both with and without consideration of the existing reserve system. Our results showed that the success of a reserve network of forests in representing different measures of biodiversity depends on the selection procedure, selection criteria, and data set used. Ad hoc selection was the worst option. A scoring procedure was generally more efficient than maximum random selection. Heuristic methods…