| 1995 - Particle Swarm Optimization: A concept for the optimization of nonlinear functions using particle swarm methodology by James Kennedy & Russell Eberhart. This is the first paper talking about PSO. |
| 1998 - Matching Algorithms to Problems: An Experimental Test of the Particle Swarm...: A paper by Jame Kennedy and William Spears |
| 1998 - Parameter Selection in Particle Swarm Optimization: A paper by Yuhui Shi and Russell C. Eberhart. |
| 1999 - A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability: Application of PSO on reactive power and voltage Control |
| 1999 - Human Tremor Analysis using Particle Swarm Optimization: A paper by R. Eberhart and X. Hu. PSO is used to train artificial neural network to classify tremor patients from normal subjects. |
| 2000 - Adapting Particle Swarm Optimization to Dynamic Environments: In this paper the authors propose a method for adapting the particle swarm optimizer for dynamic environments. The process consists of causing each particle to reset its record of its best position as the environment changes, to avoid making direction and velocity decisions on the basis of outdated information. |
| 2000 - Cooperative Learning in Neural Networks using Particle Swarm Optimizers: A paper by Frans van den Bergh and Andries P. Engelbrecht, South Africa. The interesting point is that they split the input vectors to several sub-vectors, each which is optimized cooperatively in its own swarm. |
| 2001 - Hybrid Particle Swarm Optimiser with Breeding and Subpopulations: a paper by Løvbjerg, M., Rasmussen, T., K. and Krink, T. |
| 2001 - Practical Distribution State Estimation Using Hybrid Particle Swarm Optimization: A hybrid PSO is proposed by introduction of the natural selection mechanism. The authors demonstrated hybrid PSO is better than original version on distribution state estimation problems. |
| 2002 - Adaptive Control Utilising Neural Swarming: PSO is used to adjust the network weights, with the Adaptive Neural Swarming method, the controller could adapt to environmental changes. It is tested in a real-world task of controlling a simulated non-linear bioreactor. |
| 2003 - Engineering optimization with particle swarm: PSO is used to solve engineering problems with multiple non-linear constraints. |
| 2003 - Particle swarm with extended memory for multiobjective optimization: multiobjective optimization with PSO |
| 2003 - Swarm intelligence for permutation optimization: a case study on n-queens problem: PSO is modified to deal with permutation set |
Classical
