Chai-Yeoung Jung

The optimal solution of TSP using the new mixture initialization and sequential transformation method in genetic algorithm

TSP is a problem finding out the shortest distance out of possible courses where one starts a certain city and turns back to a starting city, visiting every city only once among N cities. This paper proposes the new method using both population initialization and sequential transformation method at the same time and then proves the improvement of capability by comparing them with existing methods.

The improved initialization method of genetic algorithm for solving the optimization problem

TSP(Traveling Salesman Problem) used widely for solving the optimization is the problem to find out the shortest distance out of possible courses where one starts a certain city, visits every city among N cities and turns back to a staring city. At this time, the condition is to visit N cities exactly only once. TSP is defined easily, but as the number of visiting cities increases, the calculation rate increases geometrically. This is why TSP is classified into NPHard Problem. Genetic Algorithm is used representatively to solve the TSP. Various operators have been developed and studied until now for solving the TSP more effectively. This paper applied the new Population Initialization Method (using the Random Initialization method and Induced Initialization method simultaneously), solved TSP more effectively, and proved the improvement of capability by comparing this new method with existing methods.