Python version of the Dynamically dimensioned search allowing flexible search range (DDS-FSR) developed by Jin Y.
The DDS-FSR was modified the Dynamically dimensioned search algorithm (DDS). The DDS-FSR is purposed to efficiently solve constrained optimization problems.
The python version of the DDS-FSR was modifed the DDS-Py. The DDS-Py was coded on Aug 2015 by Thouheed Abdul Gaffoor (https://github.com/t2abdulg/DDS_Py).
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.