A class of generalized filled functions for unconstrained global optimization.
Shang, You Lin, Wang, Wei Xiang, Sun, Quan Bao (2007)
Applied Mathematics E-Notes [electronic only]
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Displaying similar documents to “Modified T-F function method for finding global minimizer on unconstrained optimization.”
A class of generalized filled functions for unconstrained global optimization.
Finding global minima with a filled function approach for non-smooth global optimization.
Wang, Weixiang, Shang, Youlin, Zhang, Ying (2010)
Discrete Dynamics in Nature and Society
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A filled function approach for nonsmooth constrained global optimization.
Wang, Weixiang, Shang, Youlin, Zhang, Ying (2010)
Mathematical Problems in Engineering
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A Hybrid Approach Combining Local Search and Constraint Programming for a Large Scale Energy Management Problem
Haris Gavranović, Mirsad Buljubašić (2013)
RAIRO - Operations Research - Recherche Opérationnelle
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This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and to produce competitive results. We also propose an efficient way to scale the method for huge instances. A large part of the presented work was done to compete in the ROADEF/EURO Challenge...
Experiences with stochastic algorithms for a class of constrained global optimisation problems
Abdellah Salhi, L. G. Proll, D. Rios Insua, J. I. Martin (2000)
RAIRO - Operations Research - Recherche Opérationnelle
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A distributed search algorithm for global optimization on numerical spaces
Pierre Courrieu (1993)
RAIRO - Operations Research - Recherche Opérationnelle
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Convex quadratic underestimation and Branch and Bound for univariate global optimization with one nonconvex constraint
Hoai An Le Thi, Mohand Ouanes (2006)
RAIRO - Operations Research
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The purpose of this paper is to demonstrate that, for globally minimize one dimensional nonconvex problems with both twice differentiable function and constraint, we can propose an efficient algorithm based on Branch and Bound techniques. The method is first displayed in the simple case with an interval constraint. The extension is displayed afterwards to the general case with an additional nonconvex twice differentiable constraint. A quadratic bounding function which is better than...
A self-adaptive trust region method for the extended linear complementarity problems
Zhensheng Yu, Qiang Li (2009)
Applications of Mathematics
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By using some NCP functions, we reformulate the extended linear complementarity problem as a nonsmooth equation. Then we propose a self-adaptive trust region algorithm for solving this nonsmooth equation. The novelty of this method is that the trust region radius is controlled by the objective function value which can be adjusted automatically according to the algorithm. The global convergence is obtained under mild conditions and the local superlinear convergence rate is also established...
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...