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A Naive multi-scale search algorithm for global optimization problems.
- Source :
-
Information Sciences . Dec2016, Vol. 372, p294-312. 19p. - Publication Year :
- 2016
-
Abstract
- This paper proposes a multi-scale search algorithm for solving global optimization problems given a finite number of function evaluations. We refer to this algorithm as the Naive Multi-scale Search Optimization ( NMSO ). NMSO looks for the optimal solution by optimistically partitioning the search space over multiple scales in a hierarchical fashion. Based on a weak assumption about the function smoothness, we present a theoretical analysis on its finite-time and asymptotic convergence. An empirical assessment of the algorithm has been conducted on the noiseless Black-Box Optimization Benchmarking (BBOB) testbed and compared with the state-of-the-art optimistic as well as stochastic algorithms. Moreover, the efficacy of NMSO has been validated on the black-box optimization competition within the GECCO’15 conference where it has secured the third place out of twenty-eight participating algorithms. Overall, NMSO is suitable for problems with limited function evaluations, low-dimensionality search space, and objective functions that are separable or multi-modal. Otherwise, it is comparable with the top performing algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 372
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 118211696
- Full Text :
- https://doi.org/10.1016/j.ins.2016.07.054