Displaying similar documents to “Finding global minima with a filled function approach for non-smooth global optimization.”

Diagonal Numerical Methods for Solving Lipschitz Global Optimization Problems

Dmitri E. Kvasov (2008)

Bollettino dell'Unione Matematica Italiana

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This paper briefly describes some results of the author's PhD thesis, which has been specially mentioned by the Italian INdAM-SIMAI Committee for the Competition "The Best PhD Thesis in Applied Mathematics defended in 2004-2006". In this work, a global optimization problem is considered where the objective function is a multidimensional black-box function satisfying the Lipschitz condition over a hyperinterval and hard to evaluate. Such functions are frequently encountered in practice...

Expériences with Stochastic Algorithms fir a class of Constrained Global Optimisation Problems

Abdellah Salhi, L.G. Proll, D. Rios Insua, J.I. Martin (2010)

RAIRO - Operations Research

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The solution of a variety of classes of global optimisation problems is required in the implementation of a framework for sensitivity analysis in multicriteria decision analysis. These problems have linear constraints, some of which have a particular structure, and a variety of objective functions, which may be smooth or non-smooth. The context in which they arise implies a need for a single, robust solution method. The literature contains few experimental results relevant to such...

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 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...

A Global Stochastic Optimization Method for Large Scale Problems

W. El Alem, A. El Hami, R. Ellaia (2010)

Mathematical Modelling of Natural Phenomena

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In this paper, a new hybrid simulated annealing algorithm for constrained global optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the basic simulated annealing algorithm (SA) that gives the region containing the global solution of an objective function. In addition, Simultaneous Perturbation Stochastic Approximation...

A new nonmonotone adaptive trust region algorithm

Ahmad Kamandi, Keyvan Amini (2022)

Applications of Mathematics

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We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstrained optimization problems. This algorithm incorporates two novelties: it benefits from a radius dependent shrinkage parameter for adjusting the trust region radius that avoids undesirable directions and exploits a new strategy to prevent sudden increments of objective function values in nonmonotone trust region techniques. Global convergence of this algorithm is investigated under some mild...

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...