Levy flight and disrupt operator-based elephant herding optimization for global optimization problems and feature selection to classify medical data

Metaheuristic techniques have gained the attention of many researchers in the last few years. These techniques are used to solve real-world optimization problems as well as for feature selection. This work proposes a levy flight and disrupt operator-based elephant herding optimization algorithm (LDEHO). In the proposed algorithm, opposition based-learning is utilized to commence with better initial positions. Levy flight and matriarch mean are introduced to update the positions of clan individuals. In addition, the disruption phenomenon is introduced to generate new individuals for replacing the worst clan individuals. Further, an elitism scheme is introduced to preserve the best search agents in consecutive iterations. The performance of the proposed LDEHO algorithm is validated on 97 benchmark functions. A comparative analysis of the proposed LDEHO algorithm with fourteen state-of-the-art algorithms has been made. Results show the high potential of the LDEHO algorithm in solving the benchmark functions. Further, Friedman's mean rank test and multiple comparison tests are applied to demonstrate the statistically significant difference between the algorithms. Moreover, a binary version of the proposed LDEHO algorithm is introduced for feature selection to classify the medical datasets. The performance of the binary LDEHO is validated on 15 medical datasets and compared with six state-of-the-art algorithms. Results show the supremacy of the binary LDEHO for feature selection to classify the medical data. Friedman's test proves that the proposed binary LDEHO algorithm is statistically different and better than other algorithms.