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
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
This direct search method does not require any knowledge
of the objective function derivatives.
The PSO is a meta-heuristic optimization process created by Kennedy and Eberhart
Three PSO are implanted in this toolbox :
* the "Inertia Weight Model" by Shi & Eberhart in 1998,
* the "Radius",
* the "BSG-Starcraft" by the author.
Sébastien Salmon is a mecatronics research engeneer and a PhD. student at the
M3M - UTBM.
He uses the PSO for actuator optimization and inverse parameter identification.
Please cite the author when using modificated PSO (Radius and/or
Please contact the author if your are satisfied of this works (or not) ans if
you find some bugs ;) .
* Kennedy, J. and Eberhart, R. C. (1995). Particle swarm optimization. Proc.
IEEE Int'l. Conf. on Neural Networks, IV, 1942–1948. Piscataway, NJ: IEEE
* Shi, Y. and Eberhart, R. C. (1998a). Parameter selection in particle swarm
optimization. In Evolutionary Programming VII: Proc. EP98, New York:
Springer-Verlag, pp. 591-600.
* Shi, Y. and Eberhart, R. C. (1998b). A modified particle swarm optimizer.
Proceedings of the IEEE International Conference on Evolutionary Computation,
69-73. Piscataway, NJ: IEEE Press.