Name
Abstract | ||
|---|---|---|
Particle Swarm Optimisation (PSO) uses a population of particles that fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both towards the best point sampled by it and towards the swarm's best point, while its momentum tries to keep it moving in its current direction.Previous research started exploring the possibility of evolving the force generating equations which control the particles through the use of genetic programming (GP).We independently verify the findings of the previous research and then extend it by considering additional meaningful ingredients for the PSO force-generating equations, such as global measures of dispersion and position of the swarm. We show that, on a range of problems, GP can automatically generate new PSO algorithms that outperform standard human-generated as well as some previously evolved ones. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1068009.1068036 | GECCO |
Keywords | Field | DocType |
genetic programming approach,best point,new pso algorithm,extended particle swarm,genetic programming,additional meaningful ingredient,fitness landscape,previous research,current direction,force generating equation,particle swarm optimisation,pso force-generating equation,particle swarm,swarm intelligence | Particle swarm optimization,Population,Mathematical optimization,Fitness landscape,Swarm behaviour,Computer science,Swarm intelligence,Multi-swarm optimization,Genetic programming,Particle | Conference |
ISBN | Citations | PageRank |
1-59593-010-8 | 28 | 1.69 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riccardo Poli | 1 | 2589 | 308.79 |
| Cecilia Di Chio | 2 | 251 | 21.24 |
| William B. Langdon | 3 | 1652 | 185.76 |