ExpensiveOptimBenchmark
Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions (by AlgTUDelft)
parmoo
Python library for parallel multiobjective simulation optimization (by parmoo)
ExpensiveOptimBenchmark | parmoo | |
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1 | 4 | |
19 | 71 | |
- | - | |
3.9 | 6.4 | |
7 months ago | 1 day ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ExpensiveOptimBenchmark
Posts with mentions or reviews of ExpensiveOptimBenchmark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-14.
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29 Python real world optimization tutorials
For the problems with continous decision variables it is not trivial to come up with faster approaches on a modern many-core CPU. But even with discrete input (scheduling and planning) new continous optimizers can compete. The trick is to utilize parallel optimization runs and numba to perform around 1E6 fitness evaluations each second. Advantage is that it is much easier to create a fitness function than for instance to implement incremental score calculation in Optaplanner. And it is more flexible if you have to handle non-standard problems. For very expensive optimizations (like https://github.com/AlgTUDelft/ExpensiveOptimBenchmark) parallelization of fitness evaluation is more important than to use surrogate models.
parmoo
Posts with mentions or reviews of parmoo.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-06.
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Multi objective optimization problems
DTLZ problems are the standard that most papers compare on. I have an implementation of most of DTLZ problems in my package (https://github.com/parmoo/parmoo). You can also find them in pymoo, and most other packages
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The Evolutionary Computation Methods No One Should Use
Then another modified DTLZ set in Python so that the solutions will have a configurable location here (https://github.com/parmoo/parmoo)
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Bayesian optimization research
I want to plug my own tool, but it is not exactly BO, but could be used for that: - https://github.com/parmoo/parmoo
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Bayesian optimization applications
I maintain an open-source Python library for doing generic surrogate-model-based global optimization (BO being the most common flavor thereof) for the Dept of Energy. https://github.com/parmoo/parmoo
What are some alternatives?
When comparing ExpensiveOptimBenchmark and parmoo you can also consider the following projects:
fast-cma-es - A Python 3 gradient-free optimization library
BayesOpt - BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.
VTMOP - Solver for Blackbox Multiobjective Optimization Problems
prima - PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.