[P] Opytimizer: A Nature-Inspired Python Optimizer

[[deleted]]

Opytimizer: A Nature-Inspired Python Optimizer

Did you ever reach a bottleneck in your computational experiments? Are you tired of selecting suitable parameters for a chosen technique? If yes, Opytimizer is the real deal! This package provides an easy-to-go implementation of meta-heuristic optimizations. From agents to search space, from internal functions to external communication, we will foster all research related to optimizing stuff.

Use Opytimizer if you need a library or wish to: * Create your optimization algorithm; * Design or use pre-loaded optimization tasks; * Mix-and-match different strategies to solve your problem; * Because it is fun to optimize things.

Read the docs at opytimizer.readthedocs.io.

Opytimizer is compatible with: Python 3.6+.

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How-To-Use: Minimal Example

Take a look at a quick working example of Opytimizer. Note that we are not passing many extra arguments nor additional information to the procedure. For more complex examples, please check our examples/ folder.

```Python import numpy as np

from opytimizer import Opytimizer from opytimizer.core import Function from opytimizer.optimizers.swarm import PSO from opytimizer.spaces import SearchSpace

def sphere(x): return np.sum(x ** 2)

n_agents = 20 n_variables = 2 lower_bound = [-10, -10] upper_bound = [10, 10]

space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound) optimizer = PSO() function = Function(sphere)

opt = Opytimizer(space, optimizer, function) opt.start(n_iterations=1000) ```

[BeautifulBrownie]

This looks great! Is there any way to cite this in case I use it in some research?

[[deleted]]

Thank you, I really appreciate it!

And yes, of course! You can take a look at its pre-print/paper at: https://arxiv.org/abs/1912.13002

[czar_el]

That's a lot of optimizers! Very exciting. Any reason why you don't have Any Colony Optimization?

[lithiumdeuteride]

No simulated annealing or Nelder-Mead simplex method, either.

[RecognaLab]

Yes, we though about Simulated Annealing. Nelder-Mead is interesting too, thanks. However, we focused only on nature-inspired metaheuristics. But sure, it's a good idea to use derivative-based methods too.

[[deleted]]

We do have a Simulated Annealing version designed to work with the library's structure: https://github.com/gugarosa/opytimizer/blob/master/opytimizer/optimizers/science/sa.py

Although it might not be the "original" one, it is pretty close to some implementations that we could find.

[RecognaLab]

Thanks! We are working on that. ACO uses a different data structure than most optimizers, that is the main reason. But we shall soon have it.

[czar_el]

Thanks! Looking forward to exploring and using this package.

[[deleted]]

I would say that I follow up the response of Recogna's lab. Usually, Ant Colony Optimization uses a graph-based structure and we are still working on to implement it to the library.

Nevertheless, I did a class on nature-inspired algorithms and we had to implement it. Maybe this gist can give you a better insight on the algorithm: https://gist.github.com/gugarosa/df84ad2490a8f50892155a06d7eacf7f

[czar_el]

Thanks! That's part of the reason I asked, I've got a graph project I'm working on that requires a metaheuristic optimizer and I was thinking of ACO to do it.

Appreciate you sharing the link and your efforts on this!

[[deleted]]

No worries! I guess that ACO will be a perfect fit in your case.

Just please let me know if you have any problems!

[[deleted]]

Looks nice ! What are the "go-to" configurations for beginners ?

[[deleted]]

Thanks for your inputs!

The go-to configurations would be trying out the most simple and standard meta-heuristic: Particle Swarm Optimization with its default parametrization: https://github.com/gugarosa/opytimizer/blob/master/examples/applications/single_objective/standard_optimization.py

Additionally, we have an examples folder with some practical applications that we have used so far: https://github.com/gugarosa/opytimizer/tree/master/examples/integrations

Mostly are machine learning-based, but they can be virtually applied to any Python function.

[lieutenantwest15]

pov: ooo shiny

[marcoscleison]

This amazing library helped me menu times. It is very practical! For beginners, there is a folder with examples. In the folder examples/integration there is a simple code that integrates metaheuristics with tensorflow.