@Article{jiot.2021.014980,
AUTHOR = {Yu Xue , Yan Zhao},
TITLE = {New Solution Generation Strategy to Improve Brain Storm Optimization  Algorithm for Classification},
JOURNAL = {Journal on Internet of Things},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {109--118},
URL = {http://www.techscience.com/jiot/v3n3/46020},
ISSN = {2579-0080},
ABSTRACT = {As a new intelligent optimization method, brain storm optimization 
(BSO) algorithm has been widely concerned for its advantages in solving 
classical optimization problems. Recently, an evolutionary classification 
optimization model based on BSO algorithm has been proposed, which proves its 
effectiveness in solving the classification problem. However, BSO algorithm 
also has defects. For example, large-scale datasets make the structure of the 
model complex, which affects its classification performance. In addition, in the 
process of optimization, the information of the dominant solution cannot be well 
preserved in BSO, which leads to its limitations in classification performance. 
Moreover, its generation strategy is inefficient in solving a variety of complex 
practical problems. Therefore, we briefly introduce the optimization model 
structure by feature selection. Besides, this paper retains the brainstorming 
process of BSO algorithm, and embeds the new generation strategy into BSO 
algorithm. Through the three generation methods of global optimal, local optimal 
and nearest neighbor, we can better retain the information of the dominant 
solution and improve the search efficiency. To verify the performance of the 
proposed generation strategy in solving the classification problem, twelve 
datasets are used in experiment. Experimental results show that the new 
generation strategy can improve the performance of BSO algorithm in solving 
classification problems.},
DOI = {10.32604/jiot.2021.014980}
}