Implementasi algoritma ant colony opitimization untuk mencari rute terbaik pengantaran barang = Implementation of ant colony optimization algorithm for best delivery route searching

Nainggolan, Verrell Panahatan (2020) Implementasi algoritma ant colony opitimization untuk mencari rute terbaik pengantaran barang = Implementation of ant colony optimization algorithm for best delivery route searching. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Dengan berkembangnya banyak taksi online dan aplikasi peta, kebutuhan AI untuk mencari jalan juga meningkat. Sudah banyak AI yang dibuat untuk mencari jalan dan algoritma Ant Colony Optimization merupakan salah satu contohnya. Travelling Salesman Problem digunakan untuk membuktikan bahwa Ant Colony Optimization dapat mencari jalan terbaik ke suatu tempat. Ant Colony Optimization meniru tingkah laku semut di dunia nyata untuk mencari rute terbaik menuju ke tujuan menggunakan feromon. Implementasi feromon adalah hal yang terpenting untuk mendapatkan hasil yang memuaskan dari algoritma Ant Colony Optimization. Untuk mendapatkan hasil terebut, ada 2 aturan yang akan digunakan, Global Pheromone Trail Update dan Local Pheromone Trail Update. Aturan-aturan tersebut menggunakan sebuah persamaan untuk menentukan jumlah feromon yang tersisa pada jalan dan jumlah feromon tersebut menentukan bagaimana semut berjalan mengitari peta. Global Pheromone Trail Update dan Local Pheromone Trail Update akan memiliki nilai konstan untuk menghitung feromon yang tersisa. Untuk mendapatkan hasil yang optimal, nilai-nilai tersebut harus disesuaikan dan jumlah semut yang digunakan juga akan dipertimbangkan. Hasil yang memuaskan dicapai dengan menggunakan 10 jumlah semut dan nilai konstan Global Pheromone Trail Update 0.7 dan Local Pheromone Trail Update 0.9. / With the development of many online taxis and maps applications, the need for a path finding AI also increases. There are many AI that are made for path findings and Ant Colony Optimization Algorithms can be one of them. Travelling Salesman Problem is used to prove that Ant Colony Optimization can find the best route to a destination Ant Colony Optimization mimics the behavior or real-life ants to find the best route to the destination using Pheromones. The implementation of pheromone is the most important aspect of achieving the desired outcome for the Ant Colony Optimization. To achieve such outcome, 2 rules are being applied to the algorithm, the Global Pheromone Trail Update and Local Pheromone Trail Update. These rules have their own equations to determine how much pheromone is left in the trail and the amount of pheromone determine how the ants move around the map. Both Global Pheromone Trail Update and Local Pheromone Trail Update will have a constant value to them to calculate how much of pheromone is left. To reach the optimal solution these values need to be adjusted as well as the number of ants that is used. Desirable result is achieved by using 10 ants with Global Pheromone Trail Update constant value of 0.7 and Local Pheromone Trail Update constant value of 0.9.

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