Development Roadmap

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.

[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]

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?