Computer Science Masters Papers

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Browsing Computer Science Masters Papers by Subject "Ant algorithms."

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    Implementation of Multilevel Thresholding Based Ant Colony Optimization Algorithm for Edge Detection of Images
    (North Dakota State University, 2017) Kanajal Chandrakanth, Spoorthy
    Edges in an image characterize object boundaries in an image, which is helpful in image processing and feature extraction in a particular scene. One of the methods used to detect edges in an image is image thresholding, which replaces a pixel in an image with black pixel if an image intensity is less than some constant T. Edge detection is used to classify, interpret and analyze the digital images in various fields such as robots, the sensitive applications in military, etc. A hierarchical multilevel thresholding method for edge detection using the Ant Colony algorithm is used in this paper. Multilevel thresholding technique is applied in this paper based on previous work done by Ashour, A. S., El-Sayed (2014). Further, the results are produced for edge detection of images using ACO algorithm with multilevel thresholding. Both Gray scale images and color images are used to evaluate the efficiency of the algorithm.
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    Neutralization of Conflict Areas Using an Ant Colony Heuristic Approach
    (North Dakota State University, 2010) Bapanpally, Pavan Kumar
    In this paper, we present a unique approach to solve the Neutralization of Conflict Areas problem using an Ant Colony Optimization technique. The Neutralization of Conflict Areas is a known problem, and over the years, considerable research has been conducted to find strategies [7] to solve the problem. To effectively deal with this kind of problem, many search algorithms and techniques have been proposed. The Ant Colony Optimization technique has been very successful in solving problems such as the travelling salesman problem, vehicle routing problem, and routing and scheduling problems. The suggested approach to the problem presented here is to find routes for conflict areas and then neutralize the conflict areas. To find routes to conflict areas, we used the concept of a Dynamic Source Routing protocol. To find the shortest paths, we used the Ant Colony Optimization technique. The goal of this paper is to suggest a swarm-based approach to find conflict areas, neutralize conflict areas, and recruit help from other resource units when needed to neutralize conflict areas. The proposed solution has been implemented in a simulator. We simulate how two different sets of cooperating ants called explorer ants and worker ants, with different operational abilities work together in finding and neutralizing conflict areas and also in getting help from other resources areas. The solution can be visualized using a graphical user interface. The framework that we implement will allow for experimentation with a wide variety of experimental parameters.
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    Speed Optimized Implementation of Ant Colony Optimization Algorithm for Image Edge Detection
    (North Dakota State University, 2016) Moparthi, Rashmi
    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.