A PERTURBED QUASI-NEWTON ALGORITHM FOR BOUND-CONSTRAINED GLOBAL OPTIMIZATION  

作　　者：Raouf Ziadi Abdelatif Bencherif-Madani 

机构地区：[1]Laboratory of Fundamental and Numerical Mathematics(LMFN),Department of Mathematics,Faculty of Sciences,University Ferhat Abbas Setif 1,Setif,Algeria

出　　处：《Journal of Computational Mathematics》2025年第1期143-173,共31页计算数学(英文)

摘　　要：This paper presents a stochastic modification of a limited memory BFGS method to solve bound-constrained global minimization problems with a differentiable cost function with no further smoothness. The approach is a stochastic descent method where the deterministic sequence, generated by a limited memory BFGS method, is replaced by a sequence of random variables. To enhance the performance of the proposed algorithm and make sure the perturbations lie within the feasible domain, we have developed a novel perturbation technique based on truncating a multivariate double exponential distribution to deal with bound-constrained problems;the theoretical study and the simulation of the developed truncated distribution are also presented. Theoretical results ensure that the proposed method converges almost surely to the global minimum. The performance of the algorithm is demonstrated through numerical experiments on some typical test functions as well as on some further engineering problems. The numerical comparisons with stochastic and meta-heuristic methods indicate that the suggested algorithm is promising.

关 键 词：Global optimization Limited memory BFGS method Stochastic perturbation Truncated multivariate double exponential distribution 

分 类 号：O17[理学—数学]