Solving the Vehicle Routing Problem with Genetic Tabu Search Algorithm

This paper aims to develop a genetic algorithm to solve a travel salesman problem (TSP). The algorithm is used to find the shortest path between the 25 cities of source and destination. In the literature the routing problem is solved by using search graph technique to find the shortest path. The main objective is to minimize the traveling cost and time. There are many researchers tried to solve this problem previously by using different methods and using different algorithms to get the better and effective solution. and in this ethic, I also work to get a better result than what they did before by using com bination GA and TS algorithms. Our proposed GA has optimized implementation that makes our algorithm faster and efficient to solve the famous Traveling Salesman Problem (TSP). when compared with the state-of-the art Tabu Search Algorithm to solve the same TSP, our genetic algorithm achieved significant improvements in terms of accuracy. An efficient MATLAB implementation makes our code ready for the deployment in environment that has constraints on the system memory and speed. The results affirmed the potential of the proposed genetic algorithm. The obtained performance is better dans genetic algorithm and Tabu search algorithm.