Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search method is computationally expensive for selecting the optimal thresholds. Therefore, a hybrid bat algorithm with genetic crossover operation and smart inertia weight (SGA-BA) is proposed to choose the optimal thresholds. Furthermore, between-class variance (the Otsu method) and Kapur’s entropy are used as objective functions. In the novel SGA-BA, the smart inertia weight balances the SGA-BA’s exploration and exploitation based on the number of iterations and fitness values. Moreover, the local search capability of SGA-BA is strengthened by the crossover operation of the genetic algorithm. Meanwhile, the random vector is replaced by the beta distribution, which updates the frequency of bat in a smart way. The proposed SGA-BA was evaluated by a set of benchmark images with various levels of thresholds. Additionally, SGA-BA was compared with some well-known and recent heuristic algorithms, such as the genetic algorithm (GA), gravitational search algorithm (GSA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved salp swarm algorithm (LSSA) and basic bat algorithm (BA). The experimental results show that the proposed SGA-BA provides better outcomes than the other algorithms.