Application of Ant Colony Optimization (ACO) Algorithm to Optimize Trans Banyumas Bus Routes

Abstract

The ever-increasing population and high mobility impact the massive number of vehicles that affect the development of public transportation and the determination of effective routes. These factors make it very important to optimize the route because it will impact operational costs and the punctuality of picking up passengers. Determining the optimal route can be categorized as a Traveling Salesman Problem (TSP). TSP is the activity of a salesman to visit each city exactly once and return to his hometown by minimizing the total cost. This study purposed to determine the optimal Trans Banyumas route by applying the Ant Colony Optimization (ACO) algorithm. ACO is an algorithm inspired by the behavior of ant colonies in searching for food by finding the shortest distance between the nest and the food source. The parameter values used in the ACO algorithm significantly affect the quality of the solution. The parameters used in this research are the maximum number of iterations, the number of ants, the pheromone evaporation constant, the pheromone intensity control, and the visibility control value. Based on the test results for the Trans Banyumas Corridor 3 using optimal parameters, the ACO algorithm found the shortest route with a total distance of 29.8 km. The determination of new corridor routes using the ACO algorithm was also successfully carried out, Corridor 4 with a distance of 30.8 km and Corridor 5 about 21.6 km.