Research on Mixed-Strategy Based Grey Wolf Optimization for Gene Expression Classification

With the advantages of easy implement and few adjustment parameters, grey wolf optimization (GWO) performs well in solving global optimization problems. However, the original GWO can suffer from slow convergence speed and plunge into local optimums in dealing with complex problems. To address these issues, improvement of mixed-strategy based grey wolf optimization (MGWO) is proposed in this paper. Firstly, we present chaos strategy in combination with quasi-opposition learning strategy for generating more high-quality population to improve convergence speed of the algorithm. Then, we embed sine cosine mechanism into the original GWO to jump out of local optimums. We utilize the interaction of sine and cosine to better balance the exploration and exploitation capabilities of the algorithm. Finally, we use benchmark functions for testing the performance of proposed algorithm. Experimental results demonstrate that proposed algorithm can improve search accuracy and convergence speed compared with other state-of-the-art algorithms. Simultaneously, to verify further the performance of proposed algorithm, classification problems on gene expression datasets are also evaluated. Both continuous and binary versions of MGWO are respectively used for completing parameters optimization and feature selection. Experimental results show that proposed algorithm has better performance than others in terms of Sensitivity, Specificity, G-mean, Accuracy and F-measure.