Introduction - If you have any usage issues, please Google them yourself
This document introduces the Particle Swarm Optimization (PSO) in Scilab.
The PSO method, published by Kennedy and Eberhart in 1995,
is based on a population of points at first stochastically
deployed on a search field.
Each member of this particle swarm could be a solution of the optimization
problem.
This swarm flies in the search field (of D dimensions) and each member
of it is attracted by its personal best solution and by the best solution
of its neighbours. Each particle has a memory storing all data relating to its
flight (location, speed and its personal best solution).
It can also inform its neighbours, i.e. communicate its speed and position.
This ability is known as socialisation. For each iteration, the objective
function is evaluated for every member of the swarm.
Then the leader of the whole swarm can be determined: it is the particle
with the best personal solution. The process leads at the end to the best
global solution.