National grid gas relink login3/16/2024 ![]() ![]() Sarma, J., De Jong, K.A.: An analysis of the effect of the neighborhood size and shape on local selection algorithms. Pitsolulis, L.S., Resende, M.G.C.: Greedy Randomized Adaptive Search Procedures. Miyamoto, K.: Plasma Physics and Controlled Nuclear Fusion. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Melab, N., Cahon, S., Talbi, E.: Grid computing for parallel bioinspired algorithms. Kluwer Academic Publishers, Boston (2003) Juhász, Z., Kacsuk, P., Kranzlmüller, D.: Distributed and Parallel Systems: Cluster and Grid Computing. In: 16th Euromicro International Conference on Parallel, Distributed and Network-based Processing, 2008 et al.: Grid computing in order to implement a three-dimensional magnetohydrodynamic equilibrium solver for plasma confinement. Golberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Glover, F.: A template for scatter search and path relinking. Cambridge University Press, Cambridge (2007) Morgan Kaufmann, San Francisco (1999)įreidberg, J.: Plasma Physics and Fusion Energy. McGraw-Hill, New York (1996)įoster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. (ed.) Parallel and Distributed Computing Handbook-Parallel Genetic Algorithms. In: International Parallel and Distributed Processing Symposium, 2003Ĭhipperfield, A., Fleming, P.: In: Zomaya, A.Y.H. Cambridge University Press, Cambridge (2003)Ĭahou, S., Talbi, E., Melab, M., Paradis, E.O.: A framework for paralleled and distributed metaheuristics. Princeton University Press, Princeton (2003)īellan, P.M.: Fundamentals of Plasma Physics. ![]() The results obtained clearly improve the configuration of existing devices.Īarts, E., Lenstra, J.K.: Local Search in Combinatorial Optimization. Because of the high complexity of the problems we are trying to optimise, the distributed paradigm as well as the number of computational resources of the grid represents an excellent alternative to carry out experiments to modelize and improve nuclear fusion devices by executing these tools. Since these applications require a high computational cost to perform their operations, the use of the grid arises as an ideal environment to carry out these tests. Here, the possibility to introduce evolutionary algorithms (EAs) like genetic algorithms (GAs) or Scatter Search (SS) to look for optimised configurations offers a great solution for some of these problems. These tools usually require a large time to finish their computations and they also use a large number of parameters to represent the behaviour of nuclear fusion devices. Some of these problems can be solved by means of modeling tools. Nuclear fusion is the next generation of energy, but many problems are still present in current nuclear fusion devices. ![]()
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