Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images

Abstract

Dragonfly optimization (DFO) is a population based meta-heuristic optimization algorithm that simulates the static and dynamic swarming behaviors of dragonflies. The static swarm comprising less number of dragonflies in a small area for hunting preys, while the dynamic swarm with a large number of dragonflies migrates over long distances; and they represent the exploration and exploitation phases of the DFO. This paper introduces a self adaptive scheme for tuning the DFO parameters and suggests a methodology involving self-adaptive DFO (SADFO) for performing multilevel segmentation of digital images. The multilevel segmentation problem is formulated as an optimization problem and solved using the SADFO. The method optimizes the threshold values through effectively exploring the solution space in obtaining the global best solution. The results of real life and medical images illustrate the performance of the suggested method.