Abstract
ABSTRACTNowadays, the Traveling Salesman Problem (TSP) is one of the most studied combinational optimization problems that researchers study. Although it is easy to define, its solution is hard. Therefore, it is one of the NP-hard problems in the research literature. It can be used to solve real-life problems such as route planning and scheduling, and transportation and logistics applications. In this study, for TSP, an interface that can run on mobile devices using Android and IOS operating systems is developed. Real-world data are used online by the interface. Locations, and the distance between them, are obtained instantly by Google Maps APIs. Genetic (GA) and ant colony optimization (ACO) algorithms are used to solve the TSP. Furthermore, users have also been allowed to conduct trials for different parameter values. The application developed has been tested on two different datasets. The test results show that for the determination of the optimum route, the ACO algorithm is better than the GA. However, when considering the run times, GA works much faster than ACO.
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