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Combinatorial Problem (combinatorial + problem)
Selected AbstractsOptimal allocation of distributed generation and reactive sources considering tap positions of voltage regulators as control variablesEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 3 2007Mohamad Esmail Hamedani Golshan Abstract In this paper, by defining and solving an optimization problem, amount of distributed generators (DGs) and reactive power sources (RSs) in selected buses of a distribution system are computed to make up a given total of distributed generation for minimizing losses, line loadings, and total required reactive power capacity. The formulated problem is a combinatorial problem, therefore Tabu search algorithm is applied for solving the optimization problem. Results of solving the optimization problem for a radial 33-bus distribution system and a meshed 6-bus system are presented. When using less amount of reactive capacity, regarding tap positions of voltage regulators as control variables has considerable role in loss reduction and improvement of voltage profile. In the case of meshed systems, including line loadings in the cost function can significantly change results of solving the optimization problem such as amount of the required reactive capacity and how to assign DGs and RSs to the selected buses. Copyright © 2006 John Wiley & Sons, Ltd. [source] A Branch-and-Prune algorithm for the Molecular Distance Geometry ProblemINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 1 2008Leo Liberti Abstract The Molecular Distance Geometry Problem consists in finding the positions in of the atoms of a molecule, given some of the inter-atomic distances. We show that under an additional requirement on the given distances this can be transformed to a combinatorial problem. We propose a Branch-and-Prune algorithm for the solution of this problem and report on very promising computational results. [source] Current Status of Virtual Combinatorial Library DesignMOLECULAR INFORMATICS, Issue 7 2005Lutz Weber Abstract Combinatorial compound libraries have become an established research tool for finding molecules with desired properties. This review gives an overview on the current status of methods, techniques and design processes for combinatorial libraries. Recent developments include the sequential and parallel use of multiple sophisticated design criteria to select suitable molecules. Diversity measures, physico-chemical and pharmaco-kinetic properties, together with a variety of target and ligand based design methods are being integrated into a seamless process. The proper choice and the implementation of design and selection tools has become a combinatorial problem by itself. However, the application of virtual library design has already yielded an astonishing increase in research productivity and is expected to become a leading tool especially for the discovery of new small molecule drug candidates in the next years. [source] Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA StudiesBIOMETRICS, Issue 1 2009Shili Lin Summary One of the major challenges facing researchers studying complex biological systems is integration of data from -omics platforms. Omic-scale data include DNA variations, transcriptom profiles, and RAomics. Selection of an appropriate approach for a data-integration task is problem dependent, primarily dictated by the information contained in the data. In situations where modeling of multiple raw datasets jointly might be extremely challenging due to their vast differences, rankings from each dataset would provide a commonality based on which results could be integrated. Aggregation of microRNA targets predicted from different computational algorithms is such a problem. Integration of results from multiple mRNA studies based on different platforms is another example that will be discussed. Formulating the problem of integrating ranked lists as minimizing an objective criterion, we explore the usage of a cross entropy Monte Carlo method for solving such a combinatorial problem. Instead of placing a discrete uniform distribution on all the potential solutions, an iterative importance sampling technique is utilized "to slowly tighten the net" to place most distributional mass on the optimal solution and its neighbors. Extensive simulation studies were performed to assess the performance of the method. With satisfactory simulation results, the method was applied to the microRNA and mRNA problems to illustrate its utility. [source] Optimization of a Process Synthesis Superstructure Using an Ant Colony AlgorithmCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 3 2008B. Raeesi Abstract The optimization of chemical syntheses based on superstructure modeling is a perfect way for achieving the optimal plant design. However, the combinatorial optimization problem arising from this method is very difficult to solve, particularly for the entire plant. Relevant literature has focused on the use of mathematical programming approaches. Some research has also been conducted based on meta-heuristic algorithms. In this paper, two approaches are presented to optimize process synthesis superstructure. Firstly, mathematical formulation of a superstructure model is presented. Then, an ant colony algorithm is proposed for solving this nonlinear combinatorial problem. In order to ensure that all the constraints are satisfied, an adaptive, feasible bound for each variable is defined to limit the search space. Adaptation of these bounds is executed by the suggested bound updating rule. Finally, the capability of the proposed algorithm is compared with the conventional Branch and Bound method by a case study. [source] On peptide de novo sequencing: a new approach,JOURNAL OF PEPTIDE SCIENCE, Issue 4 2005Dr Renato Bruni Abstract A procedure is presented for the automatic determination of the amino acid sequence of peptides by processing data obtained from mass spectrometry analysis. This is a basic and relevant problem in the field of proteomics. Furthermore, it has an even higher conceptual and applicative interest in peptide research, as well as in other connected fields. The analysis does not rely on known protein databases, but on the computation of all amino acid sequences compatible with the given spectral data. By formulating a mathematical model for such combinatorial problems, the structural limitations of known methods are overcome, and efficient solution algorithms can be developed. The results are very encouraging both from the accuracy and computational points of view. Copyright © 2004 European Peptide Society and John Wiley & Sons, Ltd. [source] A generalization of the weighted set covering problemNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 2 2005Jian Yang Abstract We study a generalization of the weighted set covering problem where every element needs to be covered multiple times. When no set contains more than two elements, we can solve the problem in polynomial time by solving a corresponding weighted perfect b -matching problem. In general, we may use a polynomial-time greedy heuristic similar to the one for the classical weighted set covering problem studied by D.S. Johnson [Approximation algorithms for combinatorial problems, J Comput Syst Sci 9 (1974), 256,278], L. Lovasz [On the ratio of optimal integral and fractional covers, Discrete Math 13 (1975), 383,390], and V. Chvatal [A greedy heuristic for the set-covering problem, Math Oper Res 4(3) (1979), 233,235] to get an approximate solution for the problem. We find a worst-case bound for the heuristic similar to that for the classical problem. In addition, we introduce a general type of probability distribution for the population of the problem instances and prove that the greedy heuristic is asymptotically optimal for instances drawn from such a distribution. We also conduct computational studies to compare solutions resulting from running the heuristic and from running the commercial integer programming solver CPLEX on problem instances drawn from a more specific type of distribution. The results clearly exemplify benefits of using the greedy heuristic when problem instances are large. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2005 [source] | |||||||||||||