MSO: a framework for bound-constrained black-box global optimization algorithms

Abdullah Al-Dujaili, S. Suresh () and N. Sundararajan
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Abdullah Al-Dujaili: Nanyang Technological University
S. Suresh: Nanyang Technological University
N. Sundararajan: Nanyang Technological University

Journal of Global Optimization, 2016, vol. 66, issue 4, No 9, 845 pages

Abstract: 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.

Keywords: Global optimization; Black-box functions; Multi-scale; Space-partitioning; Sampling; Lipschitzian; Convergence analysis (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10898-016-0441-5

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