Solving Traveling Salesman Problem for Large Spaces using Modified Meta-Optimization Genetic Algorithm

Abstract

Traveling salesman problem also called TSP is defined to find the best shortest way between n cities such as nodes, customers, and branches etc. with known distances for traveling between each city on GPS, where the salesman leaves a location in the city, visits each of the cities just once and returns back to the origin of city where he started. The traveling salesman problem is one of the NP-hard problems (nondeterministic polynomial time) in optimization. It has a wide range of applications including distribution, planning, logistics, and it has been studied by researchers and academicians for so many years. In this paper, applied Meta-optimization genetic algorithm with neural networks is used to solve the TSP for finding all the best paths between all n cities. The meta-optimization genetic algorithm is good to find the way between all cities with low progress, less time, and compared with the TSP just using a genetic algorithm with the same parameters using the same map for the cities or nodes.