GA-based applications for routing with an upper bound constraint

This paper presents a method of searching for the shortest route via the most designated points among the routes whose lengths are less than the upper bound using a genetic algorithm (GA). If chromosomes whose route lengths exceed the upper bound are simply screened out in the GA process, the optimization probability gets worse. For the purpose of solving this problem, this paper proposes a new fitness function including an upper bound constraint which can be flexibly changed during the searching process. By using this function, the optimum is efficiently obtained and the optimization probability can be raised. Furthermore, the effectiveness of the proposed method is verified by experiments, applying it to the actual map data.