Description

This package contains a collection of common benchmark problems for black-box
optimization. Under the term "black-box problem", we understand problems for
which only little is known about their structure and properties. Such problems
usually appear in practice when simulator output or some other complex system
with nonlinear behavior is to be optimized.

Contained test problems:

Binary problems OneMax, LeadingOnes, and LeadingOnesTrailingZeros and three
instance generators for multimodal problems
CEC 2005 collection of single-objective problems
CEC 2007 collection of multiobjective problems
Dixon-Szegö collection for global optimization
DTLZ problems 1-7
Multiple-Peaks Model 2
Walking Fish Group (WFG) toolkit
ZDT collection for multiobjective optimization

The infrastructure of this package can also be used to wrap your own
(real-world) optimization problems in the problem base class. Reasons to do
this may be the following features:

Support for single-objective and multi-objective problems
In general, no assumptions about the search space are made
Evaluations are automatically counted
Can use true parallelism or concurrency via multiprocessing(.dummy)
Provides functionality for checking bound constraints and repairing
violations of them in continuous optimization
Optionally: detection and subsequently avoidance of duplicate evaluations

A few real-world problems are also included. Examples:

Configuration of a gradient method on test problem
Uniformity optimization of points in the unit hypercube

Dependencies

Some modules of this package have additional dependencies on third-party
packages. However, these are not enforced during installation not to hinder
the utilization of the base module, which will always be free of dependencies.

=============== =================
Module Dependencies
=============== =================
base none
binary none
cec2005 numpy
cec2007 numpy, diversipy
continuous none
dtlz diversipy
mpm numpy
multiobjective diversipy
realworld numpy, diversipy
wfg diversipy
zdt diversipy
=============== =================

Documentation

The documentation is located at
https://www.simonwessing.de/optproblems/doc/

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optproblems

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