Development Roadmap
See original GitHub issueDevelopment 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. |
Issue Analytics
- State:
- Created 6 years ago
- Comments:11 (8 by maintainers)
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Top GitHub Comments
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!
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?