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Volume & Issue no: Volume 3, Issue 11, November 2014

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Title:
Performance Evaluation of Ant Colony Optimization Algorithm and Genetic Algorithm in Travelling Salesman Problem
Author Name:
O. D. Fenwaa , I A. Adeyanjub and O.O. Adeosun
Abstract:
ABSTRACT Traveling Salesman Problem (TSP) is a well-known, popular and extensively studied problem in the field of combinatorial optimization and attracts computer scientists, mathematicians and others. Its statement is deceptively simple, but yet it remains one of the most challenging problems in operational research. It also an optimization problem of finding a shortest closed tour that visits all the given cities within the shortest time. Several optimization techniques have been used to solve the Travelling Salesman Problems such as; Ant Colony Optimization Algorithm (ACO), Genetic Algorithm (GA) and Simulated Annealing, but comparative analysis of ACO and GA in TSP has not been carried out. In this paper, an evaluation of performance was made between the Ant Colony Optimization (ACO) and Genetic Algorithm (GA) in optimizing the nearest city and distance covered by the traveler. The simulation was done and carried out on Matlab 7.10a. The results generated show that GA is a well -accepted simulator in solving the Travelling Salesman Problem, as it out performs the ACO in terms of simulation time and distance covered. Hence GA is a useful tool in solving the travelling salesman problem, as it optimizes better than the ACO. Keywords:- Genetic Algorithm, Ant Colony Optimization, Swarm Intelligence, Pheromone
Cite this article:
O. D. Fenwaa , I A. Adeyanjub and O.O. Adeosun , " Performance Evaluation of Ant Colony Optimization Algorithm and Genetic Algorithm in Travelling Salesman Problem" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 3, Issue 11, November 2014 , pp. 243-249 , ISSN 2319 - 4847.
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