Solving A Combinatorial Optimization Problem Using Artificial Fish Swarm Algorithm
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Dr. Nitesh M Sureja, Dr. Sanjay P Patel
|DOI : 10.14445/22315381/IJETT-V68I5P206S|
MLA Style: Dr. Nitesh M Sureja, Dr. Sanjay P Patel "Solving A Combinatorial Optimization Problem Using Artificial Fish Swarm Algorithm" International Journal of Engineering Trends and Technology 68.5(2020):27-32.
APA Style:Dr. Nitesh M Sureja, Dr. Sanjay P Patel. Solving A Combinatorial Optimization Problem Using Artificial Fish Swarm Algorithm International Journal of Engineering Trends and Technology, 68(5),27-32.
In recent times, nature inspired optimization algorithms, which are normally inspired from the food foraging, security and breeding behavior of social species have been widely used for solving a variety of combinatorial optimization problems. In this context, the social behavior of fish colonies has been explored to develop a novel algorithm, the so called Artificial Fish Swarm Algorithm (AFSA), based on the behavior of fish swarm in search for food. In this paper, the AFSA is applied to a variant of the benchmark problem known as Travelling Salesman Problem (TSP). The results obtained are then compared with other nature inspired algorithms to analyse the performance of Artificial Fish Swarm Algorithm. Comparison shows that the algorithm has better convergence performance than other algorithms.
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Combinatorial Optimization, Nature Inspired Algorithms, Random Traveling Salesman Problem, Artificial Fish Swarm Algorithm