Keywords : ant colony optimization

Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images

Maha Abdul Rahman Hasso; Aseel Ismail Ali

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 63-79
DOI: 10.33899/csmj.2012.163719

Ant Colony Optimization (ACO) is a method of heuristic search using in general artificial intelligence (swarm intelligence) to simulate the behavior of the aggregate food for ants to find new solutions to the combinatorial optimization problems. Artificial ant's behavior depends on the trails of real ant with additional capabilities to make it more effective such as a memory to save the past events. Every ant build solutions to the problem, and uses the information grouped about the features and performance of the private problem, to change the look to the ant problem.
In this work, an edge detection technique based on Ant Colony Optimization is used by selecting pheromone matrix which represents the information about edges in each pixel based on the guidelines set up by the ant on the image. Multiple values for different sizes of neighbor pixels are applied and a heuristic information function to test results is proposed. The results show high accuracy in edge detection of different biomedical images with different neighbors, the proposed algorithm is implemented in C Sharp 2008 language which provides high-efficiency software visible language and speed. A comparative study is also given illustrating the superiority of the proposed algorithm.

Interleaving between Ant Colony Optimization and Tabu Search for Image Matching

Ghusoon S. Basheer

AL-Rafidain Journal of Computer Sciences and Mathematics, 2007, Volume 4, Issue 2, Pages 59-77
DOI: 10.33899/csmj.2007.164016

Image matching plays an important role in many applications such as multi-modality medical imaging and multi-spectral image analysis. The role of matching is to integrate multiple sources of object information into a single image. The matching problem consists of determining the unknown transform parameters required to map one image to match the other image(20). Different non – traditional   methods are  used for solving this kind of  problem. Among these methods are the  Genetic Algorithms, Neural Networks & Simulating Annealing.
            Swarm Intelligence (SI) algorithms take their inspiration from the collective behavior of natural, for example, ant colonies, flocks of birds, or fish shoals, a particularly successful strandant colony optimization (ACO)(1). Ant Colony Optimization is a population-based general search technique, proposed by Dorigo(1992,1996), for the  solution of difficult combinatorial problems)4). The studies show that, in nature, the ant colony is able to discover the shortest paths between the nest and food sources very efficiently, such a deposit substance is called pheromone during talking and another ants can smell it, if one of ants find a short path, it feedback on the same path and the value of pheromone on this path increases and a another ants gradually chose this path.(22)
            Tabu search  is one of the best known heuristic to choose the next neighbor to move on. At each step, one chooses the best neighbor with respect to  specific function (23).
            The basic idea in this paper is using Ant Colony Optimization(ACO) & Tabu Search(TS) as a success strategy for matching two images. The suggestion algorithm evaluation is a good promising solution, by providing an optimal algorithm which is executed by optimal time and coast, I believe that there is no prior research conjoining the two topics in this way. The program is written in Matlab language (6.5).