@inproceedings{251c9a5fb19f4a3d93c4c85a7cf42113,
title = "Dynamic population size based particle swarm optimization",
abstract = "This paper is the first attempt to introduce a new concept of the birth and death of particles via time variant particle population size to improve the adaptation of Particle Swarm Optimization (PSO). Here a dynamic particle population based PSO algorithm (DPPSO) is proposed based on a time-variant particle population function which contains the attenuation item and undulate item. The attenuation item makes the population decrease gradually in order to reduce the computational cost because the particles have the tendency of convergence as time passes. The undulate item consists of periodical phases of ascending and descending. In the ascending phase, new particles are randomly produced to avoid the particle swarm being trapped in the local optimal point, while in the descending phase, particles with lower ability gradually die so that the optimization efficiency is improved. The test on four benchmark functions shows that the proposed algorithm effectively reduces the computational cost and greatly improves the global search ability.",
keywords = "Dynamic population size, Particle swarm optimization, Population, Swarm diversity",
author = "Sun, {Shi Yu} and Ye, {Gang Qiang} and Yan Liang and Yong Liu and Quan Pan",
year = "2007",
doi = "10.1007/978-3-540-74581-5_42",
language = "英语",
isbn = "9783540745808",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "382--392",
booktitle = "Advances in Computation and Intelligence - Second International Symposium, ISICA 2007, Proceedings",
note = "2nd International Symposium on Intelligence Computation and Applications, ISICA 2007 ; Conference date: 21-09-2007 Through 23-09-2007",
}