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Convergence Rate Evaluation of Derivative-Free Optimization Techniques

  • Thomas LuxEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

This paper presents a convergence rate comparison of five different derivative-free numerical optimization techniques across a set of 50 benchmark objective functions. Results suggest that Adaptive Memory Programming for constrained Global Optimization, and a variant of Simulated Annealing are two of the fastest-converging numerical optimization techniques in this set. Lastly, there is a mechanism for expanding the set of optimization algorithms provided.

Keywords

Optimization Convergence Metaheuristic Derivative-free 

Notes

Acknowledgments

This research project was funded by the Roanoke College Mathematics Computer Science and Physics Department. The python code for AMPGO and the benchmarking library were a result of the freely available work done by Andrea Gavana at [9].

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Roanoke CollegeSalemUSA

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