Name
Abstract | ||
|---|---|---|
This work merges ideas from two very different areas: Particle Swarm Optimisation and Evolutionary Game Theory. In particular, we are looking to integrate strategies from the Prisoner Dilemma, namely cooperate and defect, into the Particle Swarm Optimisation algorithm. These strategies represent different methods to evaluate each particle's next position. At each iteration, a particle chooses to use one or the other strategy according to the outcome at the previous iteration (variation in its fitness). We compare some variations of the newly introduced algorithm with the standard Particle Swarm Optimiser on five benchmark problems. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-78761-7_63 | EvoWorkshops |
Keywords | Field | DocType |
next position,standard particle swarm optimiser,previous iteration,evolutionary game theory,different method,evolutionary game-theoretical approach,benchmark problem,particle swarm optimisation,prisoner dilemma,different area,particle swarm optimisation algorithm | Particle swarm optimization,Mathematical optimization,Evolutionary algorithm,Computer science,Prisoner's dilemma,Swarm intelligence,Multi-swarm optimization,Artificial intelligence,Evolutionary game theory | Conference |
Volume | ISSN | ISBN |
4974 | 0302-9743 | 3-540-78760-7 |
Citations | PageRank | References |
8 | 0.56 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cecilia Di Chio | 1 | 251 | 21.24 |
| Paolo Di Chio | 2 | 22 | 5.36 |
| Mario Giacobini | 3 | 576 | 61.21 |