APPLICATION OF  GENETIC  ALGORITHM  TO SOLVE TRAVELING SALESMAN PROBLEM

This research investigated the application of Genetic Algorithm capable of solving the traveling salesman problem (TSP). Genetic Algorithm are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer Simulations demonstrate that the Genetic Algorithm is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks, and evolutionary computation of the successful use of a natural metaphor to design an optimization algorithm. A study of the genetic algorithm explains its performance and shows that it may be seen as a parallel variation of tabu search, with an implicit memory. Genetic algorithm is the most efficient in computational time but least efficient in memory consumption. The Genetic algorithm differs from the nearest neighbourhood heuristic in that it considers the nearest route while the neighbourhood heuristic considers the nearest path. The Genetic algorithm requires a system with parallel architecture for its optimal implementation. The activities of each genetic algorithm should be run as a separate operating system process