Solving Prioritized Multi-Objective TSP Using Genetic Algorithm

Abstract: Genetic algorithm has been successfully adopted to solve combinatorial problems. One of which is the Travelling Salesman Problem (TSP). One of the applications of TSP is when there is a trade off between delivering goods to customers using shortest path so that it is beneficial for the service provider, and delivering it based on customer�s priority so it is beneficial for the service receiver. In this paper, a multi-objective TSP is proposed to balance between shortest path and high priority using genetic algorithm. This work is featured by proposing a new fitness function to evaluate different solutions during the process of selection and crossover. The experiment is conducted by altering the factors associated with both path length and priority. The results show that better solution is achieved when more weight is assigned to the priority than when assigned to the path length.

Keywords: Genetic algorithm, Travelling Salesman Problem, mutli-objective TSP, crossover, fitness function.