Multistart with early termination of descents
A Žilinskas, J Gillard, M Scammell… - Journal of Global …, 2021 - Springer
Multistart is a celebrated global optimization technique frequently applied in practice. In its
pure form, multistart has low efficiency. However, the simplicity of multistart and multitude of …
pure form, multistart has low efficiency. However, the simplicity of multistart and multitude of …
Optimal and sub-optimal stopping rules for the multistart algorithm in global optimization
B Betro, F Schoen - Mathematical Programming, 1992 - Springer
In this paper the problem of stopping the Multistart algorithm for global optimization is
considered. The algorithm consists of repeatedly performing local searches from randomly …
considered. The algorithm consists of repeatedly performing local searches from randomly …
Bayesian optimization with local search
Y Gao, T Yu, J Li - Machine Learning, Optimization, and Data Science: 6th …, 2020 - Springer
Global optimization finds applications in a wide range of real world problems. The multi-start
methods are a popular class of global optimization techniques, which are based on the idea …
methods are a popular class of global optimization techniques, which are based on the idea …
Stopping rules for box-constrained stochastic global optimization
IE Lagaris, IG Tsoulos - Applied Mathematics and Computation, 2008 - Elsevier
We present three new stopping rules for Multistart based methods. The first uses a device
that enables the determination of the coverage of the bounded search domain. The second …
that enables the determination of the coverage of the bounded search domain. The second …
Sequential stopping rules for the multistart algorithm in global optimisation
B Betrò, F Schoen - Mathematical Programming, 1987 - Springer
In this paper a sequential stopping rule is developed for the Multistart algorithm. A statistical
model for the values of the observed local maxima of an objective function is introduced in …
model for the values of the observed local maxima of an objective function is introduced in …
Bayesian stopping rules for multistart global optimization methods
CGE Boender, AHG Rinnooy Kan - Mathematical Programming, 1987 - Springer
By far the most efficient methods for global optimization are based on starting a local
optimization routine from an appropriate subset of uniformly distributed starting points. As …
optimization routine from an appropriate subset of uniformly distributed starting points. As …
Improved filters and randomized drivers for multi-start global optimization
L Lasdon - McCombs Research Paper Series No. IROM-06-06, 2006 - papers.ssrn.com
We describe and test 2 types of improvements to the multistart heuristic framework for
smooth global optimization presented in [8]. This starts a gradient based local NLP solver …
smooth global optimization presented in [8]. This starts a gradient based local NLP solver …
Towards “Ideal Multistart”. A stochastic approach for locating the minima of a continuous function inside a bounded domain
C Voglis, IE Lagaris - Applied Mathematics and Computation, 2009 - Elsevier
A stochastic global optimization method based on Multistart is presented. In this, the local
search is conditionally applied with a probability that takes in account the topology of the …
search is conditionally applied with a probability that takes in account the topology of the …
Pattern discrete and mixed hit-and-run for global optimization
HO Mete, Y Shen, ZB Zabinsky, S Kiatsupaibul… - Journal of Global …, 2011 - Springer
We develop new Markov chain Monte Carlo samplers for neighborhood generation in global
optimization algorithms based on Hit-and-Run. The success of Hit-and-Run as a sampler on …
optimization algorithms based on Hit-and-Run. The success of Hit-and-Run as a sampler on …
Evolutionary operators in global optimization with dynamic search trajectories
EC Laskari, KE Parsopoulos, MN Vrahatis - Numerical Algorithms, 2003 - Springer
One of the most commonly encountered approaches for the solution of unconstrained global
optimization problems is the application of multi-start algorithms. These algorithms usually …
optimization problems is the application of multi-start algorithms. These algorithms usually …