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

作　　者：Saman Babaie-Kafaki Saeed Rezaee 

机构地区：[1]Department of Mathematics,Semnan University,Semnan,Iran

出　　处：《International Journal of Intelligent Computing and Cybernetics》2019年第3期389-399,共11页智能计算与控制论国际期刊（英文）

基　　金：the anonymous reviewers for their valuable comments and suggestions helped to improve the quality of this work.

摘　　要：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.

关 键 词：Nonlinear programming Simulated annealing Adaptive radius Trust region method Unconstrained optimization 

分 类 号：O22[理学—运筹学与控制论]