A Novel Approach for Solving Travelling Salesman Problem and Compare Its Performance with Inver-Over Operator of Genetic Algorithm

The traveling salesman problem (TSP) is one of the most widely studied NP hard combinatorial optimization problems and has already solved in the semi-optimal manners using numbers of different methods. Among them, Genetic Algorithms (GA) are pre-dominating. In this paper I solve the problem with a new operator, Inver-over, for an evolutionary algorithm for the TSP. This operator outperforms all other 'genetic' operators, whether unary or binary, which was first introduced by Guo Tao and Zbigniew Michalewicz. I also propose a new algorithm for solving TSP. To get a comparative idea of the performance of these algorithms I solve same problems with the two algorithms. The performance analysis shows that my proposed algorithm produces relatively better solutions in the case of the tour length every time. But when we increase the cities it takes more time to solve than the Inver-Over operator for TSP.