<![CDATA[Ant Colony Optimization (ACO) is an algorithm used to solve optimization problems, inspired by the behavior of ant colonies in find of food sources. The main issue addressed in this study is how to implement the Ant Colony algorithm to determine the shortest route for goods distribution and to analyze the influence of the parameters α (pheromone intensity) and β (heuristic value) on the effectiveness of route search. This study used a simulation approach involving several delivery vehicles for building materials in Malang Raya. The testing was conducted using 33 delivery locations, which were then divided into five delivery clusters. The shortest routes generated by the algorithm were found to be more effective when compared to routes suggested by Google Maps. The results show that the implementation of the ACO algorithm significantly reduces travel distance, with an average effectiveness of 16.26% across the five vehicles that were tested. Parameter testing indicates that higher β values (β ≥ 5) significantly influence the search for the shortest route, while variation in α does not significantly affect the results. Thus, this study concludes that the ACO algorithm is effective in optimizing delivery routes, especially when employing the appropriate combination of parameters.]]>
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