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EVOLVING THE UPDATE STRATEGY OF THE PARTICLE SWARM OPTIMISATION ALGORITHMS.
- Source :
- International Journal on Artificial Intelligence Tools; Feb2007, Vol. 16 Issue 1, p87-109, 23p, 12 Charts, 4 Graphs
- Publication Year :
- 2007
-
Abstract
- A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
GENETIC algorithms
CHROMOSOMES
MATHEMATICAL optimization
MATHEMATICS
Subjects
Details
- Language :
- English
- ISSN :
- 02182130
- Volume :
- 16
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- International Journal on Artificial Intelligence Tools
- Publication Type :
- Academic Journal
- Accession number :
- 24099022
- Full Text :
- https://doi.org/10.1142/S0218213007003217