Name
Title | ||
|---|---|---|
Geometric particle swarm optimisation on binary and real spaces: from theory to practice |
Abstract | ||
|---|---|---|
Geometric particle swarm optimization (GPSO) is a recently introduced formal generalization of traditional particle swarm optimization (PSO) that applies naturally to both continuous and combinatorial spaces. In previous work we have developed the theory behind it. The aim of this paper is to demonstrate the applicability of GPSO in practice. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces and report extensive experimental results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1274000.1274079 | GECCO (Companion) |
Keywords | Field | DocType |
extensive experimental result,combinatorial space,real space,formal generalization,previous work,hamming space,geometric particle swarm optimization,traditional particle swarm optimization,geometric particle swarm optimisation,metric space | Particle swarm optimization,Hamming code,Mathematical optimization,Computer science,Multi-swarm optimization,Euclidean geometry,Metric space,Binary number | Conference |
Citations | PageRank | References |
2 | 0.42 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cecilia Di Chio | 1 | 251 | 21.24 |
| Alberto Moraglio | 2 | 463 | 40.85 |
| Riccardo Poli | 3 | 2589 | 308.79 |