scholarly journals PENCARIAN RUTE TERBAIK UNTUK DISTRIBUSI BANK SAMPAH MENGGUNAKAN TRAVELLING SALESMAN PROBLEM (TSP) STUDI KASUS KOTA DENPASAR

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
I Wayan Supriana
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Travelling Salesman Problem ◽  
Optimization Method ◽  
Variable Speed ◽  
Travelling Salesman ◽  
Real Situation ◽  
Bank Management ◽  
Waste Bank ◽  
Route Search

ABSTRACT <br /> A garbage bank is a place used to collect disaggregated debris. The high enthusiasm of the<br />society to become a bank customer is inversely proportional to the real situation where there are<br />still a few people who become customers of garbage bank. The problem with the community is to<br />collect their own garbage and deposit it to the garbage bank management. This garbage collection<br />process should be done optimally so that the purpose of the establishment of waste banks can be<br />achieved and the growth of garbage bank customers increases. So to overcome the problem of<br />garbage picking done the best route search for waste bank distribution using Traveling Salasmen<br />Problem (TSP). The optimization method for best path determination using genetic algorithm.<br />Genetic algorithm is a method by utilizing variable speed in each path that influence the travel<br />time in each way and utilizing natural selection process known as evolution process, cross<br />breeding process or crossover function, mutation and individual improvement. The result of the<br />best route search of waste bank distribution using Traveling Salesman Problem (TSP) shows the<br />best route that must be passed by Denpasar garbage bank in the 6th generation with 331 minutes<br />travel time.<br /> <br /> Keywords: Garbage Bank, Genetic, TSP, Crossover <br /><br />ABSTRAK <br /><br />      Bank sampah adalah suatu tempat yang digunakan mengumpulkan sampah-sampah yang<br />sudah dipilah- pilah.  Antusias  masyarakat yang  tinggi  menjadi nasabah  bank  sampah <br />berbanding  terbalik  dengan  keadaan sebenarnya dimana masih sedikit masyarakat yang menjadi<br />nasabah bank sampah. Hal yang menjadi kendala masyarakat adalah  mengumpulkan sampah<br />sendiri dan menyetornya ke pihak pengelola bank sampah. Proses pengumpulan sampah ini<br />haruslah dilakukan secara optimal agar tujuan dari dibentuknya bank sampah dapat tercapai dan<br />pertumbuhan nasabah bank sampah meningkat. Maka untuk mengatasi masalah penjemputan <br />sampah dilakukan pencarian rute terbaik untuk distribusi bank sampah menggunakan Travelling<br />Salasmen Problem (TSP). Metode optimasi untuk penentuan jalur terbaik menggunakan algoritma<br />genetika. Algoritma genetika merupakan metode dengan memanfaatkan variable kecepatan disetiap<br />jalur yang mempengaruhi waktu tempuh disetiap jalan dan memanfaatkan proses seleksi alamiah<br />yang dikenal dengan proses evolusi, proses perkawinan silang atau fungsi crossover, mutasi<br />maupun perbaikan individu. Hasil dari penelitian pencarian rute terbaik distribusi bank sampah<br />menggunakan Travelling Salesman Problem (TSP) menunjukkan rute terbaik yang harus dilalui<br />bank sampah kota Denpasar pada generasi ke 6 dengan waktu tempuh 331 menit.<br /> <br />Kata Kunci: bank sampah, genetika, TSP, crossover

Application of Genetic Algorithm on Travelling Salesman Person

2014 ◽  
Vol 1048 ◽  
pp. 526-530
Author(s):  
Sambourou Massinanke ◽  
Chao Zhu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Optimization Method ◽  
Initial Population ◽  
Travelling Salesman ◽  
Minimum Path ◽  
Minimum Number ◽  
Permutation Problem ◽  
The City ◽  
Survival Of The Fittest

GA (Genetic algorithm) is an optimization method based on operators (mutation and crossover) utilizing a survival of the fittest idea. They are utilized favorably in various problems. (TSP) Travelling salesman problem is one of the famous studied. TSP is a permutation problem in which the aim is to determine the shortest tour between n different points (cities), otherwise, the problem aims to find a route covering all cities where that the total distance is minimal. In this study a single salesman travels to each of the cities and close the loop by returning to the city he started, the aim of this study is to determine the minimum number of generations in which salesman does the minimum path, cities are chosen at random as initial population. The new generations are then created iteratively till the proper path is attained.

scholarly journals Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Maha Ata Al-Furhud ◽  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Travelling Salesman ◽  
Complex Optimization ◽  
Problem Instances ◽  
Search Approach ◽  
Multiple Travelling Salesman Problem

The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

2013 ◽  
Vol 411-414 ◽  
pp. 2013-2016 ◽  
Author(s):  
Guo Zhi Wen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Algorithm Animation ◽  
The Traveling Salesman Problem

The traveling salesman problem is analyzed with genetic algorithms. The best route map and tendency of optimal grade of 500 cities before the first mutation, best route map after 15 times of mutation and tendency of optimal grade of the final mutation are displayed with algorithm animation. The optimal grade is about 0.0455266 for the best route map before the first mutation, but is raised to about 0.058241 for the 15 times of mutation. It shows that through the improvements of algorithms and coding methods, the efficiency to solve the traveling problem can be raised with genetic algorithms.

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