SUMMARY
Traffic congestion can be overcome by public transport. The optimal implementation of public transport is necessary to determine a best route. To get optimal route of public transport, it is necessary to do some combination experiments between the distance from the starting point and the destination. So that the problem can be said as a combinatoric problem. VRP is a combinatoric problem. Therefore, the problem can use a metaheuristic method.In this studies, Modified Particle Swarm Optimization algorithm with a Hyper-heuristic approach used to solve problem public transport routes. Data used is the Mumford and Mandl dataset used in several previous studies. Research was conducted by comparing results of the solutions generated by proposed methods with results of previous studies. Therefore, can find out the advantages and disadvantages of proposed methods.Based on this studies, MPSO-GI algorithm with the Hyper-Heuristics approach can be implemented and solve an UTRP. MPSO-GI algorithm with Hyper-Heuristics approach succeeded in improving hill-climbing solutions in almost all datasets with stable values. MPSO-GI algorithm with the Hyper-Heuristics approach are superior in producing passenger cost solutions on the Mandl4, Mandl6, Mandl7, Mandl8 datasets and operator costs on the Mandl4 and Mandl6 datasets when compared to previous studies.