Thanks for the link, Udit,

If there is some new algorithm that you think NLopt should adopt, can you 
please file a github issue with the suggestion and references?

> On Aug 16, 2017, at 6:52 AM, Udit Kumar <udit.v...@gmail.com> wrote:
> 
> Dear NLopt-discuss members,
> 
> Our recent publication titled "Benchmarking NLopt and state-of-the-art 
> algorithms for continuous global optimization via IACOR" has been published 
> in Elsevier's Swarm and Evolutionary Computing (DOI: 
> https://doi.org/10.1016/j.swevo.2015.10.005 
> <https://doi.org/10.1016/j.swevo.2015.10.005>).
> 
> Abstract:
> This paper presents a comparative analysis of the performance of the 
> Incremental Ant Colony algorithm for continuous optimization (IACOR), with 
> different algorithms provided in the NLopt library. The key objective is to 
> understand how various algorithms in the NLopt library perform in combination 
> with the Multi-Trajectory Local Search (Mtsls1) technique. A hybrid approach 
> has been introduced for the local search strategy, by the use of a parameter 
> that allows for probabilistic selection between Mtsls1 and the NLopt 
> algorithm. In case of stagnation, a switch is made based on the algorithm 
> being used in the previous iteration. This paper presents an exhaustive 
> comparison on the performance of these approaches on Soft Computing (SOCO) 
> and Congress on Evolutionary Computation (CEC) 2014 benchmarks. For both sets 
> of benchmarks, we conclude that the best performing algorithm is a hybrid 
> variant of Mtsls1 with BFGS for local search.
> 
> We hope the paper would be beneficial to the members of this list as a 
> reference for selection of algorithms to use, as well as a benchmark to 
> assess their performance on optimization functions of similar nature.
> 
> Thanks
> Dr. Udit Kumar
> IIT-Delhi, India
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