@inbook{d9faafd3bd6b42d6a6ac53ce756ffe0f,
title = "Brain storm optimization in objective space algorithm for multimodal optimization problems",
abstract = "The aim of multimodal optimization is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. In this paper, brain storm optimization in objective space (BSO-OS) algorithm is utilized to solve multimodal optimization problems. Our goal is to measure the performance and effectiveness of BSO-OS algorithm. The experimental tests are conducted on eight benchmark functions. Based on the experimental results, the conclusions could be made that the BSO-OS algorithm performs good on solving multimodal optimization problems. To obtain good performances on multimodal optimization problems, an algorithm needs to balance its global search ability and solutions maintenance ability.",
keywords = "Brain storm optimization, Brain storm optimization in objective space, Multimodal optimization, Swarm intelligence",
author = "Shi Cheng and Quande Qin and Junfeng Chen and Wang, \{Gai Ge\} and Yuhui Shi",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
doi = "10.1007/978-3-319-41000-5\_47",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "469--478",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}