Displaying similar documents to “Adjoint variable method for the study of combined active and passive magnetic shielding.”

Towards a theory of practice in metaheuristics design: A machine learning perspective

Mauro Birattari, Mark Zlochin, Marco Dorigo (2006)

RAIRO - Theoretical Informatics and Applications

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A number of methodological papers published during the last years testify that a need for a thorough revision of the research methodology is felt by the operations research community – see, for example, [Barr (1995) 9–32; Eiben and Jelasity, 582–587; Hooker, (1995) 33–42; Rardin and Uzsoy, (2001) 261–304]. In particular, the performance evaluation of nondeterministic methods, including widely studied metaheuristics such as evolutionary...

High-performance simulation-based algorithms for an alpine ski racer's trajectory optimization in heterogeneous computer systems

Roman Dębski (2014)

International Journal of Applied Mathematics and Computer Science

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Effective, simulation-based trajectory optimization algorithms adapted to heterogeneous computers are studied with reference to the problem taken from alpine ski racing (the presented solution is probably the most general one published so far). The key idea behind these algorithms is to use a grid-based discretization scheme to transform the continuous optimization problem into a search problem over a specially constructed finite graph, and then to apply dynamic programming to find an...

Editorial

Clarisse Dhaenens, Patrick Siarry, El-Ghazali Talbi (2008)

RAIRO - Operations Research

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Consistency checking within local search applied to the frequency assignment with polarization problem

Michel Vasquez, Audrey Dupont, Djamal Habet (2003)

RAIRO - Operations Research - Recherche Opérationnelle

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We present a hybrid approach for the Frequency Assignment Problem with Polarization. This problem, viewed as Max-CSP, is treated as a sequence of decision problems, CSP like. The proposed approach combines the Arc-Consistency techniques with a performed Tabu Search heuristic. The resulting algorithm gives some high quality solutions and has proved its robustness on instances with approximately a thousand variables and nearly ten thousand constraints.