MPBOA-A novel hybrid butterfly optimization algorithm with symbiosis organisms search for global optimization and image segmentation

The conventional Butterfly Optimization Algorithm (BOA) does not appropriately balance the exploration and exploitation characteristics of an algorithm to solve present-day challenging optimization problems. For the same, in this paper, a novel hybrid BOA (MPBOA, in short) is suggested, where the BOA is combined with mutualism and parasitism phases of the Symbiosis Organisms Search (SOS) algorithm to enhance the search behaviour (both global and local) of BOA. The mutualism phase is applied with the global phase of BOA, and the parasitism phase is added with the local phase of BOA to ensure a better trade-off between the global and local search of the proposed algorithm. A suit of twenty-five benchmark functions is employed to investigate its performance with several other state-of-the-art algorithms available in the literature. Also, to check its performance statistically, the Friedman rank test and t-test are carried out. The consistency of the proposed algorithm is tested with a boxplot diagram. Also, four real-world problems are solved to check the efficiency of the algorithm in solving industrial problems. Finally, the proposed MPBOA is utilized to obtain the optimal threshold in the multilevel thresholding problem of the segmentation of individual images. From the obtained results, it is found that the overall performance of the newly introduced MPBOA is satisfactory in terms of its search behaviour and convergence time to obtain global optima.

Automated summary

A novel hybrid BOA algorithm, MPBOA, is proposed in this paper, combining the exploration and exploitation characteristics of BOA with mutualism and parasitism phases of the SOS algorithm. The algorithm shows satisfactory performance in terms of search behavior and convergence time on twenty-five benchmark functions.