PENERAPAN ALGORITMA ANT SYSTEM DALAM MENEMUKAN JALUR OPTIMAL PADA TRAVELING SALESMAN PROBLEM (TSP) DENGAN KEKANGAN KONDISI JALAN

• Main Authors: , Andhi Akhmad Ismail, ST., , Ir. Samiadji Herdjunanto, M.Sc.
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2012
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/100564/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57115
• Institution: Universitas Gadjah Mada

The completion of Traveling Salesman Problem (TSP) is to find the shortest path to visit all of the cities. With the shortest path, it is expected that the travel time will also be shorter. In fact, when a salesman visits all of the cites in his list, he will find obstacles such as poor road conditions, congestion, damaged roads, or other constraints. Therefore, although the shortest path has been established, if there is an obstacle the travel time to all cities will be longer. One way to solve the TSP is by ant algorithm. From a lot of research that have been done, the road conditions are always considered to be fine without disturbance. The research used the simplest ant algorithm, it is the Ant System to solve TSP with constraint road conditions. The modifications were made to the Ant System by providing constraint pheromone to each road which could not be passed and also gave a long distance to the roads that should not be passed. With this modification, it is expected that the ants never pass the roads which have constraint. Therefore, the ants will find another path to visit all the cities. The data which is used for research are square grid 3x3 to 6x6, and two data from http://www.iwr.uniheidelberg. de/groups/comopt/software/TSPLIB95/, that is the ulysses16 and ulysses22 The results of this study indicate that the ants never pass constrained sections, for square grid data also two data from TSPLIB95. This occurs because the segments were given constraints, the pheromone were weighted 0 and given the longest distance. More constraints make the computational time longer.