Image segmentation via multilevel thresholding using
hybrid optimization algorithms
We introduce an alternative hybrid swarm algorithm for image segmentation that employs multilevel
thresholding techniques. For the hybridization, we have combined the whale optimization algorithm (WOA)
and the particle swarm optimization (PSO). The proposed method is called WOAPSO, and it operates in a
cooperative environment, where the initial population is divided into two subpopulations (the first subpopulation
is assigned for WOA and the other is assigned for PSO). Then, the WOA and the PSO operate in parallel during
the iterative process to update the solutions and the best solution is selected from the union of the updated
subpopulations according to the objective function. Here, two objective functions are used, the Otsu’s method
and the fuzzy entropy method. These functions evaluate the quality of the thresholds generated by the WOAPSO
considering the variance and the entropy of the classes where the pixels are cataloged. The experimental results
and comparisons provide evidence of the ability of the proposed WOAPSO algorithm to reduce the time
complexity without affecting the accuracy of the solutions.
