From Theory to Practice with Particle Swarm Optimization, Using Python

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There may be a joke that cracks me up:

“Did that, before the clock was invented, people needed to actively roam around and ask people the time?”

There may be obviously no need to clarify the joke, but when we were to overthink it a bit bit (like good mathematicians do) we will say that the joke is in regards to the incontrovertible fact that the knowledge of a particle of a bunch will be used to tell all the opposite particles. This idea is definitely way deeper than the joke I just said and will be exploited further.

Let’s consider a self-organized system, reminiscent of bird flocking or fish education. We are able to define this method as one manufactured from particles (e.g. a particle is a bird). We may assume with degree of approximation that these particles move around in space adjusting their positions based on two aspects:

  • The very best position that the precise particle knows: what the bird thinks is best for themselves.
  • The global best position that’s given by all of the particles “communicating” with one another: what the bird is instructed to do by the “predominant bird”
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