Multilevel Image Thresholding based on Particle Swarm Optimization Algorithm with Chaotic Cognitive and Social Acceleration Coefficients

This paper introduces a novel chaotic strategy for controlling the acceleration coefficients within the particle swarm optimization (PSO) algorithm. The PSO algorithm with chaotic exponential-based acceleration coefficients is developed to enhance the exploration of the search space and avoid premature convergence, a common issue associated with the standard PSO algorithm. The PSO algorithm with chaotic exponential-based acceleration coefficients is applied to multilevel image thresholding. The attained experimental results demonstrate that the PSO algorithm utilizing the chaotic strategy to control the cognitive and social acceleration coefficients can be successfully utilized for the selection of image thresholds across a variety of images.