swMATH ID: 35041
Software Authors: Al-Dujaili, Abdullah; Suresh, S.; Sundararajan, N.
Description: MSO: a framework for bound-constrained black-box global optimization algorithms. 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.
Homepage: https://link.springer.com/article/10.1007/s10898-016-0441-5
Keywords: global optimization; black-box functions; multi-scale; space-partitioning; sampling; Lipschitzian; convergence analysis
Related Software: MCS; LGO; minpack; MultiGLODS; MOPSO; COCO; SMS-EMOA; MultiMin; RBFOpt; CTA; KNITRO; SymPy; Global Optimization Toolbox For Maple
Cited in: 6 Documents

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