A modified particle swarm optimizer
Indiana University – Purdue University Indianapolis · University of Indianapolis
Abstract
Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals…
Citation impact
- FWCI
- 106.59
- Percentile
- 100%
- References
- 9
Authors
2Topics & keywords
- Particle swarm optimization
- Evolutionary computation
- Crossover
- Mathematical optimization
- Population
- Computer science
- Mutation
- Evolutionary algorithm
- Partnerships for the goals