Speed Optimized Implementation of Ant Colony Optimization Algorithm for Image Edge Detection

Abstract

Ant Colony algorithm (ACO) is an approach used to provide a solution to an optimization problem. ACO follows the mechanism adapted by Ants to search for optimal paths by performing combined activity of all ants in the colony. Ants adopt a probabilistic approach to solve problems of path discovery and alike. The behavior of ants has been mapped to a scientific algorithm to solve optimization problems. Different modified optimization variants have been run on the basic algorithm that resulted in in efficient and effective systems for solving different optimization problems including in the area of image processing. Study in this paper is applying ACO algorithm to solve the problem of image edge detection by modifying the algorithm to improve its efficiency and speed. The algorithm has been implemented in MATLAB and its speed has been enhanced by about 40-50 percent using the vectorization of different processes of the algorithm.