Python version of the Dynamically Dimensioned Search Algorithm Developed by Thouheed A.G.

REFERENCES FOR THIS ALGORITHM:

For continuous optimization problems:
Tolson, B. A. and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, doi:10.1029/2005WR004723.

For discrete/mixed integer optimization problems (Discrete DDS):
Tolson, B. A., M. Asadzadeh, H. R. Maier, and A. Zecchin (2009), Hybrid discrete dynamically dimensioned search (HD-DDS) algorithm for water distribution system design optimization, Water Resources Research, 45, W12416, doi:10.1029/2008WR007673.

README:

This file contains the user instructions for the Dynamically Dimensioned Search (DDS) Algorithm by Bryan A. Tolson.
DDS is an n-dimensional, heuristic, probabilistic global optimization algorithm for continuous/discrete/mixed decision-variable, box-constrained (bound-constrained) optimization problems.
DDS is designed to find good solutions quickly and requires no algorithm parameter tuning.

** CURRENTLY UNDER DEVELOPMENT ** WARNING: This package is still being tested