A peak-over-threshold search method for global optimization

DOI10.1016/j.automatica.2017.12.002zbMath1387.93141OpenAlexW2780168153MaRDI QIDQ1640234

Siyang Gao, Zhengjun Zhang, Shi, Leyuan

Publication date: 14 June 2018

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2017.12.002

zbMATH Keywords

global optimizationextreme value analysismetaheuristic algorithmpartition-based random search

Mathematics Subject Classification ID

Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59) Stochastic systems in control theory (general) (93E03) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (3)

SMGO-\(\Delta\): balancing caution and reward in global optimization with black-box constraints ⋮ Maximum independent component analysis with application to EEG data ⋮ SMGO: a set membership approach to data-driven global optimization

Uses Software

• simannf90
• ismev

Cites Work

• Unnamed Item
• Optimization by Simulated Annealing
• A general class of branch-and-bound methods in global optimization with some new approaches for concave minimization
• Statistical inference using extreme order statistics
• On the estimation and application of max-stable processes
• Nested Partitions and Its Applications to the Intermodal Hub Location Problem
• Parameter and Quantile Estimation for the Generalized Pareto Distribution
• Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here
• Adaptive partitioned random search to global optimization
• Nested Partitions Method for Global Optimization
• Nested partitions method for stochastic optimization
• An introduction to statistical modeling of extreme values

This page was built for publication: A peak-over-threshold search method for global optimization