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- Title
MSO: a framework for bound-constrained black-box global optimization algorithms.
- Authors
Al-Dujaili, Abdullah; Suresh, S.; Sundararajan, N.
- Abstract
This paper addresses a class of algorithms for solving bound-constrained black-box global optimization problems. These algorithms partition the objective function domain over multiple scales in search for the global optimum. For such algorithms, we provide a generic procedure and refer to as multi-scale optimization ( MSO). Furthermore, we propose a theoretical methodology to study the convergence of MSO algorithms based on three basic assumptions: (a) local Hölder continuity of the objective function f, (b) partitions boundedness, and (c) partitions sphericity. Moreover, the worst-case finite-time performance and convergence rate of several leading MSO algorithms, namely, Lipschitzian optimization methods, multi-level coordinate search, dividing rectangles, and optimistic optimization methods have been presented.
- Subjects
GLOBAL optimization; ALGORITHMS; MULTISCALE modeling; STOCHASTIC convergence; MATHEMATICAL optimization
- Publication
Journal of Global Optimization, 2016, Vol 66, Issue 4, p811
- ISSN
0925-5001
- Publication type
Article
- DOI
10.1007/s10898-016-0441-5