TY  - JOUR
TI  - Particle swarm optimization
AU  - James Kennedy
AU  - R.C. Eberhart
PY  - 2002
DO  - 10.1109/icnn.1995.488968
UR  - https://doi.org/10.1109/icnn.1995.488968
AB  - A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
ER  -