Citation Number
3
Views
17
Downloands
4
Gezgin Satıcı Problemi (GSP), bir satıcının bütün şehirleri sadece bir defa ziyaret ederek başlangıç noktasına dönmesini sağlayan en kısa rotanın belirlendiği problemdir. GSP, araç rotalamadan baskılı devre kartı montajına kadar birçok problemin temelini oluşturur. Bu problem, optimizasyon alanında çalışan kişilerden büyük ilgi görmüştür, ancak özellikle büyük ölçekli veri kümeleri için çözülmesi zordur. Bu çalışmada, GSP’nin çözümü için Akışkan Genetik Algoritma, En Yakın Komşu ve 2-Opt sezgiselleri üzerine kurulu melez bir yöntem sunulmaktadır. Önerilen yöntemin performansı literatürde bulunan En Yakın Komşu, Genetik Algoritma, Tabu Arama, Karınca Kolonisi Optimizasyonu ve Ağaç Fizyolojisi Optimizasyon algoritmaları kullanılarak elde edilen çözüm değerleri ile kıyaslanmıştır. Önerilen yöntemin sonuçları çözüm süresi ve kalitesi bakımından üstünlük göstermektedir.
The traveler seller problem (GSP) is the problem in which the shortest route is determined, allowing a seller to return to the starting point by visiting all the cities only once. GSP is the foundation of many problems, from vehicle rotating to printed circuit card installation. This problem has received great interest from people working in the optimization field, but it is difficult to solve especially for large-scale data sets. In this study, the fluid genetic algorithm for the solution of GSP is presented with a mixed method based on the nearest neighbor and 2-Opt intuitives. The performance of the proposed method is compared with the solutions value obtained using the nearest neighbor, genetic algorithm, taboo search, bear colony optimization and tree physiology optimization algorithms found in literature. The results of the proposed method are superior in terms of the time and quality of the solution.
Travelling Salesman Problem (TSP) is a problem in which the shortest possible route enabling a salesman to return to the starting point after visiting all the cities exactly once is determined. Travelling Salesman Problem is the basis for many problems ranging from vehicle routing to printed circuit boards assembly. This problem has been attracting great attention from researchers in the field of optimization; nevertheless it is difficult to solve TSP, especially for large-scale data sets. This paper presents a hybrid solution method based on Fluid Genetic Algorithm, Nearest Neighbor and 2-Opt methods for the solution of TSP. The performance of the proposed method is evaluated with the solution values of the Nearest Neighbor, Genetic Algorithm, Tabu Search, Ant Colony Optimization and the Tree Physiology Optimization algorithms in the literature. The solution results show the superiority of the proposed method in terms of solution time and quality.
Field : Mühendislik
Journal Type : Ulusal
Article Detail 
Citation Owners
Similar Articles
Download PDF
Journal Information
Listen Paper
Print This Page
Share
Mendeley
