| Adam Erskine |
| Thu 10 Mar 2016, 12:45 - 13:45 |
| 4.31/33 |
If you have a question about this talk, please contact: Steph Smith (ssmith32)
The particle swarm optimisation (PSO) algorithm is a popular metaheuristic used to solve search and optimisation type problems. Originally inspired by bird flocking it is simple to implement and continues to be used and studied. It requires parameter tuning to obtain good solutions to a given problem. The parameters control the balance between exploration and exploitation of a problem space. Analysis of PSO as a random dynamical system predicts that there exists a locus of parameter values that lead to optimum performance. Here, we outline this approach and provide supporting empirical evidence.