[SciPy-User] OT: global optimization, hybrid global local search

[josef.pktd at gmail.com]

I'm looking at nonlinear regression, function estimation again.
I'm interested in combining global search with local optimizers, which
I think should be much faster in many of our problems.

Anyone with ideas, experience, code?


I just searched and browsed a few papers, but it's not my specialization.

"In particular, the problems which are very hard to be solved by gradient
descent-based methods, or the ones which have computationally
expensive objective functions are very good candidates to be solved
by the proposed methods."   *)

*) Hybrid optimization with improved tabu search
http://www.sciencedirect.com/science/article/pii/S1568494610001511

Genetic and Nelder–Mead algorithms hybridized for a more accurate
global optimization of continuous multiminima functions
http://www.sciencedirect.com/science/article/pii/S0377221702004010

A hybrid method combining continuous tabu search and Nelder–Mead
simplex algorithms for the global optimization of multiminima
functions http://www.sciencedirect.com/science/article/pii/S0377221703006301

Hybridizing exact methods and metaheuristics: A taxonomy
http://www.sciencedirect.com/science/article/pii/S0377221708003597
mostly integer programming

Hybrid simulated annealing and direct search method for nonlinear
unconstrained global optimization
http://www.tandfonline.com/doi/abs/10.1080/1055678021000030084


Josef
(Why does scipy not have any good global optimizers?)