GA, AS, ACS VE MMAS ALGORİTMALARI PERFORMANSLARININ GEZGİN SATICI PROBLEMİ ÇÖZÜMÜ ÜZERİNDE DEĞERLENDİRİLMESİ

Year 2017, Volume: 9 Issue: 2, 50 - 60, 30.07.2017

Tuncay Aydoğan

Abstract

Gezgin Satıcı Problemi (GSP) bir çok alanda kendisine uygulama bulmuş önemli bir optimizasyon problemidir.
Bu çalışmada, sezgisel optimizasyon algoritmalarından Genetik Algoritma (GA), Karınca Sistemi (Ant SystemAS/ANT),
Karınca Koloni Sistemi (Ant Colony System-ACS) ve Max-Min Karınca Sistemi (Max-Min Ant
System-MMAS) algoritmaları ile GSP çözülerek, çözümlerin performansları incelenmiştir. Algoritmaların
tamamı bir arayüz üzerinde bulunmaktadır. Arayüzde istenilen sayıda rastgele oluşturulan noktalar (şehirler) ile
haritalar oluşturulabilmekte veya hazır kütüphanelerden veri seti yüklenebilmektedir. Bu algoritmaların
performansları maliyet (yol uzunluğu) ve tekrar sayısı olarak görülebilmektedir. Algoritmalar 36, 56, 76, 101 ve
150 nokta (şehir)’den oluşan 5 harita üzerinde denenmiştir. Her harita çözümünde en az maliyetli çözümü
MMAS, en yüksek maliyetli çözümü GA’nın oluşturduğu görülmüştür. Sıralama azdan yükseğe doğru MMAS,
AS, ACS ve GA biçiminde gerçekleşmiştir. Algoritmaların TSPLIB kütüphanesi içerindeki ch150 veri seti için
performansları literatür ile karşılaştırılmış GA, AS ve MMAS’de daha düşük maliyetlere ulaşıldığı görülmüştür.

Keywords

Genetik Algoritma, Karınca Kolonisi Optimizasyonu, Gezgin Satıcı Problemi

GA, AS, ACS AND MMAS ALGORITHMS PERFORMANCE EVALUATION ON TRAVELING SALESMAN PROBLEM SOLVING

Abstract

Travelling Salesman Problem (TSP) is an important optimization method that have been applied to various areas.
In this study, TSP is solved by using several heuristic algorithms like Genetic Algorithm (GA), Ant System
(AS/ANT), Ant Colony System (ACS) and Max-Min Ant System (MMAS), performances of these algorithms
are then measured. Applied algorithms are implemented inside an interface. Using this interface, maps can be
generated with as much as required random points (cities) or loaded from dataset. Performance criterion of the
measurement may be seen as the cost (path length) and the number repetitions. Performance measurements of
these algorithms are tested on 5 different maps consisting of 36, 56, 76, 101 and 150 points. At each test for
solving TSP for each map, the least cost is observed for MMAS and the highest cost is observed for GA.
Ascending sort of these algorithms based on their cost is observed as MMAS (least), AS, ACS and GA (highest).
The performance of the algorithms for the ch150 dataset in the TSPLIB library was found to be lower in GA, AS
and MMAS compared to the literature.

Keywords

Genetic Algorithm, Ant Colony Optimization, Travel Salesman Problem

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