Development Roadmap

[ljvmiranda921]

Development Roadmap

Here is the feature list needed for the major release. We will be implementing different PSO variants, more test functions, and a hyperparameter search utility.

Optimizers

• Binary PSO (pyswarms.single.BPSO) [1]
• Vanilla Multi-Objective PSO (pyswarms.multi.MOPSO) [2]
• Constrained PSO (pyswarms.constrained.CPSO) [3]

Test Functions

Most of the good test functions can be found here. If you want to implement a single function, just make a pull request, and then implement it. In fact, even Wikipedia (I know, I know) has a good resource for test functions.

Single-objective pyswarms.utils.single_obj

• Beale's Function
• Goldstein–Price function
• Booth's function
• Bukin function N.6
• Matyas function
• Lévi function N.13
• Schaffer function N. 2

Multi-Objective pyswarms.utils.multi_obj

• Binh and Korn function
• Chakong and Haimes function
• Fonseca and Fleming function
• Test function 4
• Schaffer function N. 1
• Schaffer function N. 2

Constrained Problems pyswarms.utils.constrained_obj

• Rosenbrock function constrained with a cubic and a line
• Rosenbrock function constrained to a disk
• Mishra's Bird function - constrained
• Townsend function
• Simionescu function

Utilities

• Hyperparameter search methods (grid search and random search)

Examples

We need various examples or use-cases to help a user use PySwarms. For now, these things are much better written in a Jupyter Notebook.

No.	Reference
[1]	J. Kennedy and R.C. Eberhart, "A discrete binary version of the particle swarm algorithm," in IEEE International Conference on Systems, Man, and Cybernetics, 1997.
[2]	M.R. Sierra and C.A. Coello, "Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art," International Journal of Computational Intelligence Research, 2006.
[3]	Parsopoulos, Konstantinos E., and Michael N. Vrahatis. "Particle swarm optimization method for constrained optimization problems," Intelligent Technologies–Theory and Application: New Trends in Intelligent Technologies pp. 214-220, 2006.

[Carl-K]

I'll do Beale's Function!

[ljvmiranda921]

Hey @Carl-K! Go ahead! Just leave a message in #6 if you ran into any problem/confusion 👍 😄

[Carl-K]

Hello again @ljvmiranda921, I can take care of the rest of the functions (goldstein, booth, bukin6, matyas, levi, schaffer2) and also add extra tests for correct input dimensions (Beale, Goldstein, Booth, etc.) if needed.

[ljvmiranda921]

Hey @Carl-K , wow, that would be really helpful! You can implement the rest of the functions in single_obj.py for now.

I forgot to mention, it would be really nice if we follow PEP8 standards in writing equations. So for the Beale's function, we can write something like this:

# Line breaks before the operator, notice the parenthesis enveloping 
# our equation.
j = ((1.5 - x_ + x_ * y_)**2.0 
    + (2.25 - x_ + x_ * y_**2.0)**2.0 
    + (2.625 - x_ + x_ * y_**3.0)**2.0)

With regard to the extra tests, I believe I have updated them already in the InputDimFail class (check this commit). Right now we're still in Pre-Alpha. Once we do a release, we'll start refactoring all our AssertionErrors into raised Exceptions. But it's still a long way but I'm taking notes on what to change so far so we won't refactor a lot of error-handling in the future. So yeah, just take the assertions and unit tests as it is for now. 👍

Anyways, good job and I hope you're enjoying! Just drop any questions here if needed!

[Carl-K]

Hello @ljvmiranda921 thank you for the guidance it is much helpful. I would just like to bring something to your attention about the tests you have written regarding

with self.assertRaises(AssertionError):

consider

def test_beale_bound_fail(self):
    x = np.array([[-5, 5]])
    x_ = np.array([[3, 0.5]])

    with self.assertRaises(AssertionError):
        fx.beale_func(x)
        fx.beale_func(x_)

vs

def test_beale_bound_fail(self):
    x = np.array([[-5, 5]])
    x_ = np.array([[3, 0.5]])

    with self.assertRaises(AssertionError):
        fx.beale_func(x)

    with self.assertRaises(AssertionError):
        fx.beale_func(x_)

for Beale's function. The array [3, 0.5] does not raise an assertion error within beale_func(x) because it is valid input, so the test for bounds should fail. In the first example the test does not fail even though it should because I am assuming "with self.assertRaises(AssertionError):" catches an assertion thrown from any line, not all of them, which is what the test currently is. In the second example the test does fail, which I believe is what you want. I hope this is not nitpicky it is just something that caught my eye.

[ljvmiranda921]

Hmmm... interesting. Turns out that context managers (with self.assertRaises(AssertionError)) work in an any perspective. Already fixed it (and the others) in the latest commit.

Thanks for catching it! 👍

[SioKCronin]

Hi @ljvmiranda921 I'm a CS grad student just beginning to use PSO in my work, and can help out with documentation and the Jupyter examples notebook.

[ljvmiranda921]

Hi @SioKCronin! That's awesome!

Just fork this repo and do a pull request once you're done. The steps can be seen in this link

Also, if you have questions, need help, or found some bugs, please don't hesitate to contact/raise an issue!

Thank you so much!

[abougouffa]

Hi @ljvmiranda921, I'm working on a project in which I implemented another PSO variant named GLIR-PSO and I can port it to work with pyswarms.

I don't know if you are interested in implementing existing PSO variants or just implement a generic framework?

[ljvmiranda921]

Hi @abougouffa, sure please do port it here in PySwarms! The idea is that we can pool different variants in the library to ease benchmarking for researchers. 👍

However, please do check if the base classes can support GLIR-PSO. Hopefully it does, if there's a need to create a new base class, or improve the existing base classes, just write a message here!

[abougouffa]

OK, great, I will port it when I get some free time 😃.

Thank you for your responsiveness 😃