TAILIEUCHUNG - Hybrid of genetic algorithm and continuous ant colony optimization for optimum solution

This research proposes a hybrid approach by combining genetic algorithm (GA) and Continuous Ant Colony Optimization (CACO) to find optimum solutions, using a continuous ant colony algorithm as a mutation of genetic algorithm; the performance of the hybrid algorithm is illustrated using three test functions. The results show the efficiency and capabilities of the new hybrid algorithm in finding the optimum solutions. | International Journal of Computer Networks and Communications Security C , , JANUARY 2014, 1–6 Available online at: ISSN 2308-9830 N C S Hybrid of Genetic Algorithm and Continuous Ant Colony Optimization for Optimum Solution BAN and ADEEBA 1 Prof. Research & Artificial Techniques Department, College of Computer Science and Mathematics, Mosul University, Mosul, Iraq 2 M. Sc. Student, Computer Science Department, College of Computer Science and Mathematics, Mosul University, Mosul, Iraq E-mail: , 2dalyadiamond@ ABSTRACT This research proposes a hybrid approach by combining genetic algorithm (GA) and Continuous Ant Colony Optimization (CACO) to find optimum solutions, using a continuous ant colony algorithm as a mutation of genetic algorithm; the performance of the hybrid algorithm is illustrated using three test functions. The results show the efficiency and capabilities of the new hybrid algorithm in finding the optimum solutions. Keywords: Genetic algorithm (GA), Continuous Ant Colony Optimization (CACO). 1 INTRODUCTION The first evolutionary-based technique introduceed in the literature was the genetic algorithms (GAs), GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach nearoptimum solutions to large problems, the GAs technique has been used in many applications in science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with Gas [1]. Ant Colony Optimization (ACO) was developed by Dorigo et al. Based on the fact that ants are able to find the shortest route between their nest and a source of food. This is done using pheromone trails, which ants deposit whenever they travel, as a form of indirect .

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