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Our approach utilizes a more number of artificial ants than traditional ACO. We increased the dependence of heuristic information when compared to general ACO and also modified the heuristic function. | ISSN:2249-5789 Kesava Rao Seerapu et al , International Journal of Computer Science & Communication Networks,Vol 2(4), 468-472 A Modified ACO algorithm for Image Edge Detection Kesava Rao Seerapu1, R. Srinivas2 *(Department of CSE, AITAM TEKKALI, INDIA) Email: kesav546@gmail.com) ABSTRACT ANT colony optimization (ACO) is a meta-heuristic optimization algorithm , inspired by occurrence of the natural phenomenon [1],[2],that ants deposit pheromone in on their way in order to mark some favorable path that should be followed by other ants of the colony. ACO has different applications like Traveling Sales Person problem. Now we propose a modified ACO on image edge detection problem, where our aim is to extract the edge information from the image. Our approach utilizes a more number of artificial ants than traditional ACO. We increased the dependence of heuristic information when compared to general ACO and also modified the heuristic function. pheromone matrix τ (N ) . ACO method mainly depends upon on matrix p(n) and the update of the pheromone matrix τ(n). we discuss about p(n) matrix and pheromone matrix. First, the k-th ant in the nth construction-step moves from the node i to the node j. The movement is in accordance with probabilistic action rule and is given by INTRODUCTION ACO algorithms are developed by observing ant colonies, they find the shortest path to the food. In order to exchange information about shortest path, ants move by leaving a chemical trail of pheromone for other ants in the colony. Ants to follow the path with more pheromone. an ant chooses a path with a probability that is proportional to number of ants that already traversed the path. Hence, individual ants, following very simple rules, interact to produce an intelligent behavior at the higher level of the ant colony. Our method uses the ant colony system. The edge information in our proposed method is directly extracted by ACO and thus it finds the best solution of the above problem
