A Literature Survey of Benchmark Functions For Global Optimization Problems

Published on Aug 19, 2013in arXiv: Artificial Intelligence
· DOI :10.1504/IJMMNO.2013.055204
Momin Jamil1
Estimated H-index: 1
Xin-She Yang1
Estimated H-index: 1
Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.
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