Name
Abstract | ||
|---|---|---|
Particle Swarm Optimization is fundamentally a stochastic algorithm, where each particle takes into account noisy information from its own history as well as that of its neighborhood. Though basic information-theoretic principles would suggest that less noise indicates greater certainty, the momentum term is simultaneously the least directly-informed and the most deterministically applied. This dichotomy suggests that the typically confident treatment of momentum is misplaced, and that swarm performance can benefit from better-motivated processes that obviate momentum entirely. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2598394.2598490 | GECCO (Companion) |
Keywords | Field | DocType |
global optimization,particle swarm optimization | Particle swarm optimization,Mathematical optimization,Certainty,Swarm behaviour,Computer science,Multi-swarm optimization,Momentum,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher K. Monson | 1 | 134 | 14.77 |
| Kevin D. Seppi | 2 | 335 | 41.46 |