Particle Swarm Optimization (PSO) from scratch. Simplest explanation in python
Before talking about swarms and particles, let's briefly discuss optimization itself. Basically, optimization is the process of finding the minima or maxima of some function. For instance, when you need to get to your office ASAP and think about which way is the fastest, you're optimizing your route (in this case it's a function). In math, there are literally hundreds of ways of optimization, and among them a sub-group called nature-inspired exists.
Nature-inspired algorithms are based on phenomena which draw inspiration from natural phenomena or processes. Among the most popular ones are Genetic Algorithm, Cuckoo Search, Ant Colony and Particle Swarm Optimization [1] or PSO.
This tutorial is implemented in python using only numpy and matplotlib. To follow up you can use this notebook.
Let's start with creating a function which we'll be optimizing using PSO.
If someone asks me to think about functions, the first thing that comes to my mind (it almost reflexive
