New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification

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.