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 N 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. This direct search method does not require any
knowledge of the objective function derivatives.
