Feature Selection of BOF Steelmaking Process Data Based on Denary Salp Swarm Algorithm

Abstract

In order to eliminate irrelevant or redundant data and improve the accuracy of basic oxygen furnace (BOF) steelmaking endpoint prediction model, a novel denary version of the salp swarm algorithm (SSA) is proposed in this paper and applied for feature selection of BOF steelmaking process data in wrapper mode. SSA is one of the recently proposed algorithms, which is inspired by the swarming behavior of salps in deep water. Firstly, the proposed denary SSA presets the dimension of solutions instead of the strategy of indeterminate number that will lead to different results over various runs. Then the native and binary versions of SSA are applied to generate candidates for leader salp; meanwhile, a probability function is utilized in DSSA to replace each element of leader salp. Finally, an update strategy for follower salps is used to enhance the exploitation of the SSA algorithm. The proposed method is employed to find the optimal solution that maximizes the regression accuracy and minimizes the non-repeatability of the feature selection on BOF steelmaking process data. The performance of the proposed approach is compared with various state-of-the-art approaches in terms of different assessment criteria. Results show that the proposed denary SSA approach of feature selection provides the repeatable results and obtains higher regression accuracy.