Born and death is the nature of lives, but most swarm intelligence algorithm did not reflect this important property. Based on Particle Swarm Optimization, the concept of life span is introduced to control the activity generation of particles. Furthermore, the differential operator is applied to enhance the convergence and precision. The performance of propose algorithm, along with PSO and DE, is tested on benchmark functions. Results show that life span and differential operator greatly improved PSO and with well-balanced exploration and exploitation characteristic. © 2011 Springer-Verlag.
CITATION STYLE
Zhang, Y. W., Wang, L., & Wu, Q. D. (2011). Mortal particles: Particle swarm optimization with life span. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6728 LNCS, pp. 138–146). https://doi.org/10.1007/978-3-642-21515-5_17
Mendeley helps you to discover research relevant for your work.