An improved brain storm optimization algorithm with new solution generation strategies for classification

Abstract

In recent years, brain storm optimization (BSO) algorithm has received much attention in solving classical optimization problems and is used to implement evolutionary classification models. However, in practical applications, large-scale datasets complicate the structure of the classification model, which can have a great impact on the classification performance. In the optimization process, the traditional single-strategy BSO cannot preserve the information of dominant solution well, and its generation strategy is inefficient in solving various complex practical problems. To solve this problem, we introduce feature selection to improve the optimization model structure. Meanwhile, in order to enhance the search capability of BSO, three new generation strategy are embedded in the BSO algorithm in this paper. With the three generation methods of global optimal, local optimal and nearest neighbor, the information of the dominant solution can be better preserved and the search efficiency can be improved. The performance of the proposed generation strategy in solving classification problems is demonstrated on ten datasets with different sizes and dimensions. The experimental results reveal that the new generation strategy can enhance the performance of BSO algorithm for solving classification problems.