Name
Abstract | ||
|---|---|---|
Previous stability analysis of the particle swarm optimizer was restricted to the assumption that all parameters are nonrandom, in effect a deterministic particle swarm optimizer. We analyze the stability of the particle dynamics without this restrictive assumption using Lyapunov stability analysis and the concept of passive systems. Sufficient conditions for stability are derived, and an illustrative example is given. Simulation results confirm the prediction from theory that stability of the particle dynamics requires increasing the maximum value of the random parameter when the inertia factor is reduced. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TEVC.2005.857077 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
maximum value,illustrative example,passive system,inertia factor,restrictive assumption,previous stability analysis,particle swarm optimizer,lyapunov stability analysis,deterministic particle swarm optimizer,particle dynamic,predictive models,lyapunov function,particle swarm optimization,deterministic approach,particle motion,circle criterion,stability,feedback,genetic algorithms,evolutionary algorithm,stochastic processes,swarm intelligence,stability analysis,neural network | Particle swarm optimization,Lyapunov function,Mathematical optimization,Circle criterion,Swarm intelligence,Lyapunov stability,Stochastic process,Deterministic system (philosophy),Mathematics,Magnetosphere particle motion | Journal |
Volume | Issue | ISSN |
10 | 3 | 1089-778X |
Citations | PageRank | References |
144 | 12.27 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| V. Kadirkamanathan | 1 | 355 | 39.25 |
| Kirusnapillai Selvarajah | 2 | 152 | 15.11 |
| P. J. Fleming | 3 | 298 | 66.79 |