Test Functions Index
Users: 1 - Average Rating: 5.00
This page contains the general index of the benchmark problems used to test different Global Optimization algorithms. It also shows some statistics on the “difficulty” of a multi-modal test problem, based on the average successful minimization across all the Global Optimizers tested in this benchmark exercise.
Scientific Area:
Optimization
Language/Environments:
C, C++, MatLab, Octave, Other, Python, R
Target Group:
Advanced, Basic
Author of the review:
Beatriz Flamia
Instituto Politécnico de Bragança
You have to login to leave a comment. If you are not registered click here
Eligius Hendrix
The description tries to do a systematic investigation into the difficulty of optimization problems versus algorithms. One of my arguments over the last 25 years is that the dificulty depends on what type of algorithm we apply and what criteria or target we want to reach. In the end, the reseach question is what types of algorithms are better for whcih characterization of instances. See for instance Baritompa, W.P. and Hendrix, E.M.T. (2005), On the Investigation of Stochastic Global Optimization Algorithms, Journal of Global Optimization, 31, 567-578