Improving Amphetamine-type Stimulants drug classification using chaotic-based time-varying binary whale optimization algorithm

A new chaotic time-varying binary whale optimization algorithm (CBWOA_TV) is introduced in this paper to optimize the feature selection process in Amphetamine-type Stimulants (ATS) and non-ATS drugs classification. Two enhancement methods were introduced in this study to provide a fit balance between exploration and exploitation in standard WOA. Firstly, a non-linear time-varying modified Sigmoid transfer function is used as the binarization method. Second, a hybrid Logistic-Tent chaotic map is employed to substitute the pseudorandom numbers of the probability operator in standard WOA. Specific high-dimensional molecular descriptors of ATS and non-ATS drugs were employed to evaluate the efficiency of the proposed algorithm. Experimental results and statistical analysis indicate that the proposed CBWOA_TV algorithm can prevent the problem of stagnation and entrapment in local minima in WOA. As a result, optimal descriptors were selected contributing to enhanced classification performance.