A randomized nonmonotone adaptive trust region method based on the simulated annealing strategy for unconstrained optimization

Abstract

Purpose

The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems.

Design/methodology/approach

The well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region (TR) algorithm.

Findings

An adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula. Also, a (heuristic) randomized adaptive TR algorithm is developed for solving unconstrained optimization problems. Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods.

Practical implications

The algorithm can be effectively used for solving the optimization problems which appear in engineering, economics, management, industry and other areas.

Originality/value

The proposed randomization scheme improves computational costs of the classical TR algorithm. Especially, the suggested algorithm avoids resolving the TR subproblems for many times.

Citation

Babaie-Kafaki, S. and Rezaee, S. (2019), "A randomized nonmonotone adaptive trust region method based on the simulated annealing strategy for unconstrained optimization", International Journal of Intelligent Computing and Cybernetics, Vol. 12 No. 3, pp. 389-399. https://doi.org/10.1108/IJICC-12-2018-0178