MSO 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 Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year MSO: a framework for bound-constrained black-box global optimization algorithms. Zbl 1394.90466Al-Dujaili, Abdullah; Suresh, S.; Sundararajan, N. 2016 all top 5 Cited by 7 Authors 4 Al-Dujaili, Abdullah 2 Sundararajan, Narasimhan 1 Khodabandelou, Ghazaleh 1 Nakib, Amir 1 Price, Christopher John 1 Robertson, B. L. 1 Wong, C. S. Y. Cited in 2 Serials 3 Information Sciences 3 Journal of Global Optimization Cited in 2 Fields 6 Operations research, mathematical programming (90-XX) 1 Computer science (68-XX) Citations by Year