[mlpack] GSoC 2018: Particle Swarm Optimization

[Chintan Soni]

Hello everyone,

My name is Chintan Soni and I'm a 4th year CS student at Pune Institute of
Computer Technology, Pune, India.

I would like to apply for the GSoC 2018 project on adding the Particle
Swarm Optimizer for constrained and unconstrained problems.
I have some prior experience working on PSO ([1], [2]) and would love to
work on it yet again.
The first link is the project where we train a basic Neural Net using PSO.
It is still a work in progress. The second link is just a bare bones
implementation of PSO.

I am also currently interning at Nvidia Pune (till ~10 May, 2018) from
where I have experience working with somewhat complex C++ code and TMP, so
understanding the mlpack codebase shouldn't be much of a problem.

I have followed the getting started links provided on the GSoC 2018 wiki
page and have built mlpack, and intend to tinker with it and go through the
source code once I'm done with the ongoing exams (by 10 March, 2018).

Another interesting idea I would like to present is the use of gradient
descent with PSO in a hybrid optimization approach ([3]: the repo belongs
to my project mate). As of now it gives better training results, but
neither of out approaches have been tested enough to actually comment on
the matter yet.

Regarding the constrained optimization problem, I've come across an idea of
using PSO to solve Max-CSPs in the past (I cannot find a link to the paper
as of now but I'll try looking it up). Is that a step in the right
direction? Also, could you please provide references to other approaches
for the same? (Especially if you have anything specific in mind.)

I will draft a proposal as soon as possible. Really looking forward to
working on the project. Thanks in advance for your help.

Regards,
Chintan Soni

Links used:
[1] https://github.com/chintans111/ANNPSO
[2] https://github.com/chintans111/SPSO
[3] https://github.com/munagekar/nnpso
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