Displaying similar documents to “Convex quadratic underestimation and Branch and Bound for univariate global optimization with one nonconvex constraint”

Parallelization of artificial immune systems using a massive parallel approach via modern GPUs

Khun, Jiří, Šimeček, Ivan

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Parallelization is one of possible approaches for obtaining better results in terms of algorithm performance and overcome the limits of the sequential computation. In this paper, we present a study of parallelization of the opt-aiNet algorithm which comes from Artificial Immune Systems, one part of large family of population based algorithms inspired by nature. The opt-aiNet algorithm is based on an immune network theory which incorporates knowledge about mammalian immune systems in...

Differential evolution algorithm combined with chaotic pattern search

Yaoyao He, Jianzhong Zhou, Ning Lu, Hui Qin, Youlin Lu (2010)

Kybernetika

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Differential evolution algorithm combined with chaotic pattern search(DE-CPS) for global optimization is introduced to improve the performance of simple DE algorithm. Pattern search algorithm using chaotic variables instead of random variables is used to accelerate the convergence of solving the objective value. Experiments on 6 benchmark problems, including morbid Rosenbrock function, show that the novel hybrid algorithm is effective for nonlinear optimization problems in high dimensional...

Deterministic global optimization using interval constraint propagation techniques

Frederic Messine (2004)

RAIRO - Operations Research - Recherche Opérationnelle

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The purpose of this article is to show the great interest of the use of propagation (or pruning) techniques, inside classical interval Branch-and-Bound algorithms. Therefore, a propagation technique based on the construction of the calculus tree is entirely explained and some properties are presented without the need of any formalism (excepted interval analysis). This approach is then validated on a real example: the optimal design of an electrical rotating machine.