A stabilized simulated annealing-based Barzilai–Borwein method for the solution of unconstrained optimization problems

The Barzilai–Borwein method offers efficient step sizes for large-scale un-constrained optimization problems. However, it may not guarantee global convergence for nonquadratic objective functions. Simulated annealing-based on Barzilai–Borwein (SABB) method addresses this issue by in-corporating a simulated annealing rule. This work proposes a novel step-size strategy for the SABB method, referred to as the SABBm method. Furthermore, we introduce two stabilized variants: SABBstab and SABBmstab. SABBstab combines a simulated annealing rule with a sta-bilization step to ensure convergence. SABBmstab builds upon SABBstab, incorporating the modified step size derived from the SABBm method. The effectiveness and competitiveness of the proposed methods are demon-strated through numerical experiments on CUTEr benchmark problems.

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-09-01
DOI
https://doi.org/10.22067/ijnao.2024.86481.1379
Primary Topic
Advanced Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
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article

A stabilized simulated annealing-based Barzilai–Borwein method for the solution of unconstrained optimization problems

SHILAP Revista de lepidopterología
Advanced Optimization Algorithms Research
article

A stabilized simulated annealing-based Barzilai–Borwein method for the solution of unconstrained optimization problems

article en

Abstract

The Barzilai–Borwein method offers efficient step sizes for large-scale un-constrained optimization problems. However, it may not guarantee global convergence for nonquadratic objective functions. Simulated annealing-based on Barzilai–Borwein (SABB) method addresses this issue by in-corporating a simulated annealing rule. This work proposes a novel step-size strategy for the SABB method, referred to as the SABBm method. Furthermore, we introduce two stabilized variants: SABBstab and SABBmstab. SABBstab combines a simulated annealing rule with a sta-bilization step to ensure convergence. SABBmstab builds upon SABBstab, incorporating the modified step size derived from the SABBm method. The effectiveness and competitiveness of the proposed methods are demon-strated through numerical experiments on CUTEr benchmark problems.

SHILAP Revista de lepidopterología
Openalex Percentile: Top 55%
Advanced Optimization Algorithms Research
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