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Whale optimization algorithm is a newly proposed bio-inspired optimization technique introduced in 2016 which imitates the hunting demeanor of humpback whales. In this paper, to enhance solution accuracy, reliability and convergence speed, we have introduced some modifications on the basic WOA structure. First, a new control parameter, inertia weight, is proposed to tune the impact on the present best solution, and an improved whale optimization algorithm (IWOA) is obtained. Second, we assess IWOA with various transfer functions to convert continuous solutions to binary ones. The proposed algorithm incorporated with the K-nearest neighbor classifier as a feature selection method for identifying feature subset that enhancing the classification accuracy and limiting the size of selected features. The proposed algorithm was compared with binary versions of the basic whale optimization algorithm, particle swarm optimization, genetic algorithm, antlion optimizer and grey wolf optimizer on 27 common UCI datasets. Optimization results demonstrate that the proposed IWOA not only significantly enhances the basic whale optimization algorithm but also performs much superior to the other algorithms.

Dimensionality Reduction Using an Improved Whale Optimization Algorithm for Data Classification