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Development Roadmap

See original GitHub issue

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.

Issue Analytics

  • State:closed
  • Created 6 years ago
  • Comments:11 (8 by maintainers)

github_iconTop GitHub Comments

1reaction
ljvmiranda921commented, Aug 21, 2017

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!

1reaction
abougouffacommented, Aug 21, 2017

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?

Read more comments on GitHub >

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