New features of Version 3.1
Output file processing
FirstCharacterAt has been introduced,
which is needed to parse the EnergyPlus 7.0 standard output files
(eso files). See the
Section 11.1.1 Initialization File.
New features of Version 3.0
GenOpt has been changed to use parallel computation to evaluate the
objective functions. With the default settings, depending on the algorithm,
GenOpt may run as many simulations in parallel as there are CPUs on your computer.
The maximum number of parallel simulations can be adjusted (see manual).
EquMesh has been rewritten to also allow discrete parameters
and continuous parameters that are logarithmically spaced. As this allows for other grids
in the space of independent parameters than only equidistant grids, the algorithm has been renamed.
It is now called
This algorithm has been deleted, use instead the algorithm
which implements a parallelized version of the Hooke Jeeves algorithm.
All algorithms have been revised to use parallel computation for evaluating the
cost function. The implementations is such that no more simulations are required than
in GenOpt 2.1, i.e., there are no speculative function evaluations at points that may not
be needed. Such speculation may lead in some cases to a shorter optimization time, at the expense
of more resource usage.
New features of Version 2.1
Release of source code
GenOpt 2.1 including its source code is released under a
modified BSD license.
New and updated example files
The EnergyPlus example files have been updated to EnergyPlus version 2.2.0.
Example files and configuration files are now also provided for Mac OS X and for IDA 3.0.
Better integration into file explorer
GenOpt is now distributed as a Java Archive (JAR) file that allows starting GenOpt from a file explorer by double-clicking the JAR file.
Update to Java 1.5
GenOpt has been updated to Java 1.5
New features of Version 2.0
Capability to Process Discrete Independent Variables
GenOpt can now process discrete independent variables, such as different window constructions,
for solving optimization problems with (continuous and) discrete independent variables
and for doing parametric studies.
New Optimization Algorithms
The following optimization algorithms are new in GenOpt 2.0:
These algorithms are members of the family of Generalized Pattern Search (GPS)
algorithms. They can be used to solve optimization problems with
continuous independent variables.
Both algorithms can be run using multiple starting points to increase the chance
of finding the global minimum if the cost function has several local minima.
An algorithm that approximates gradients by finite differences
and uses the Armijo line search algorithm.
These algorithms are members of the family of Particle Swarm Optimization
algorithms which are global heuristic optimization algorithms.
They can be used to solve optimization problems with continuous and/or discrete
This is a hybrid global optimization algorithm that initially does a
Particle Swarm Optimization for continuous and discrete independent variables
and then switches to the Hooke-Jeeves Generalized Pattern
Search algorithm to refine the continuous independent variables.
Pre- and Post-Processing
Some simulation programs, such as EnergyPlus,
do not have the capability to pre-process the independent variables,
or to post-process values that are computed during the simulation.
For such situations, input function objects and
output function objects
can now be used without having to modify GenOpt's source code.