Paper Info
Title
On Local Convergence of Stochastic Global Optimization Algorithms
Abstract
In engineering optimization with continuous variables, the use of Stochastic Global Optimization (SGO) algorithms is popular due to the easy availability of codes. All algorithms have a global and local search character, where the global behaviour tries to avoid getting trapped in local optima and the local behaviour intends to reach the lowest objective function values. As the algorithm parameter set includes a final convergence criterion, the algorithm might be running for a while around a reached minimum point. Our question deals with the local search behaviour after the algorithm reached the final stage. How fast do practical SGO algorithms actually converge to the minimum point? To investigate this question, we run implementations of well known SGO algorithms in a final local phase stage.
Year
DOI
Venue
2021
10.1007/978-3-030-86976-2_31
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT V
Keywords
DocType
Volume
Stochastic global optimization, Evolutionary algorithms, Convergence, Nonlinear optimization
Conference
12953
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Eligius M. T. Hendrix113926.97
Ana Maria A. C. Rocha212317.14