A New Multiobjective Genetic Algorithm for Route Selection

Abstract

In the area of Intelligent Transport Systems, the multiobjective route selection problem (mRSP) becomes an important key problem that needs to be solved in car navigation system (CNS). In this paper, we propose an effective route selection approach for solving mRSP while minimizing driving distance, driving time and driving cost simultaneously. A new multiobjective genetic algorithm (moGA) called distance Pareto Genetic Algorithm (dpGA) is presented to effectively solve mRSP. The mechanism of the proposed dpGA guarantees good convergence toward the Pareto-optimal front and gives sufficient emphasis on the diversity feature. The fitness function used in dpGA is based on two kinds of distance values, i.e. Pareto distance and crowding distance. Finally, we demonstrate the applicability and evaluate the efficiency of the proposed solution approach by using numerical experiments with the real digital road map data. The experimental results show the effectiveness of the proposed solution approach.