A DYNAMIC APPROACH FOR TRAVELING SALESMAN PROBLEM USING GENETIC ALGORITHMS

TSP is a challenging and popular problem from combinatorial optimization. TSP is often tackled with well known heuristic genetic algorithm. Due to the nature of the TSP, traditional GA’s stay poor when competing with other approaches. Traditional crossover and mutation operators do not satisfy TSP needs. These operators mostly end up with illegal tours. For this reason, researchers proposed many adaptive elements and cooperation of other algorithms. When the logic of GA is combined with these elements, high quality solutions both in time and path length are obtained. In this research, we analyze successful elements from the literature to use them efficiently in a novel algorithm. We also propose a new selection method which works well with our operators. We extend the abilities of greedy crossover and untwist local operator to utilize in our hybrid approach. Multiple populations collaborate together to achieve better solutions. According to the experimen-tal results, proposed novel elements outperforms their counterparts in the TSP literature. Multiple population approach provides better quality solutions. i