Hybridizing Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques

Abstract

Ant colony optimization is a swarm intelligent algorithm that mimics the ant behaviors to optimize solutions for hard optimization problems. Over years Ant-based algorithms have been used in solving different problems including: Traveling Salesman Problem (TSP), Wireless Sensors Network (WSN), Benchmark Problem, and it has been used in various image processing applications. In the image processing fields various techniques have been used to detect edges in a digital image such as Canny and Sobel edge detectors.  This Study, proposed a hybridized Ant Colony Optimization algorithm for optimizing the edge detector quality. The proposed method initializes its attribute matrix and the information at each pixel routed by ants on the input image.  Experimental results show the results of the proposed algorithm and compare the results with the original built-in MATLAB edge detection method called Canny and the results of basic Aco edge detector. All three algorithms tested in different images and the MSE and PNSR are calculated before and after applying Gaussian noise. Based on the Experimental results obtained by the three used methods (Canny Edge Detector, Ant Colony Optimization, and Hybrid Aco-Canny), the proposed Hybrid ACO-CANNY methods was the best method for detecting edges.