@inproceedings{823f5d36ed1d4f0b8ec3b79c62004a20,
title = "The multiple population co-evolution PSO algorithm",
abstract = "In order to overcome the standard particle swarm optimization algorithm which is easily trapped in local minima and optimize the shortcoming of low precision, this paper proposed a way which can make multiple information exchange between particles come true: the multiple population co-evolution PSO algorithm. This paper proposes a multiple population co-evolutionary algorithm to achieve communication among populations, and then show the feasibility and effectiveness of this algorithm through experiments.",
keywords = "Co-evolution, PSO multiple population, Particle swarm",
author = "Xuan Xiao and Qianqian Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 5th International Conference on Advances in Swarm Intelligence, ICSI 2014 ; Conference date: 17-10-2014 Through 20-10-2014",
year = "2014",
doi = "10.1007/978-3-319-11897-0_49",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "434--441",
editor = "Ying Tan and Yuhui Shi and Coello, {Carlos A. Coello}",
booktitle = "Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Proceedings",
address = "Germany",
}