Combined Mutation Operators of Genetic Algorithm for the Travelling Salesman Problem

In this study two combined mutation operators have been developed to increase the performance of Genetic Algorithm that helps to find the minimum cost in the known Travelling Salesman problem (TSP).In order to do this the combined mutation operators which are named as Inverted Exchange and Inverted Displacement are compared with four existing mutation operators. These are programmed in C++ and algorithms, ten bench mark test problems and five variations of order crossover are used. Based implemented on a set of benchmark test problems taken from the TSPLIB. The crossover operator used here is the order crossover using two of its existing and three of its new variations. In general six mutations on the numerical and graphical analysis it indicates that the Inverted Displacement has a definite supremacy over the existing four mutations except for few instances considered here. The Inversion mutation is second best performer. All optimal values found in the six mutations are obtained when the new variations of order crossover are used.