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|Title:||Theoretical and practical convergence of a self-adaptive penalty algorithm for constrained global optimization|
|Author(s):||Costa, M. Fernanda P.|
Francisco, Rogério Brochado
Rocha, Ana Maria A. C.
Fernandes, Edite Manuela da G. P.
|Journal:||Journal of Optimization Theory and Applications|
|Abstract(s):||This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems.|
|Appears in Collections:||CAlg - Artigos em revistas internacionais/Papers in international journals|
CMAT - Artigos em revistas com arbitragem / Papers in peer review journals