Traveling Salesman Problem Optimization with Local Search Section Updated Bees Algorithm

Öz

Solving traveling salesman problems with many connections with classical optimization methods is challenging. In this context, meta-heuristic algorithms that direct their searches to an optimum solution are preferred. In this study, the Bee Algorithm, one of these meta-heuristic algorithms inspired by the foraging methods of honey bees, was examined. The study aims to increase the Bees Algorithm's effectiveness in solving traveling salesman problems. The Variable Multiple Insertion operator has been added to the Classical Bee Algorithm, and optimizations have been made for different traveling salesman problems by searching within close neighborhood regions. As a result of the tests, it was seen that much better results were obtained with this algorithm compared to other Bee Algorithms in the literature. The algorithm has reduced from 1.40-2.80% to 0.11-0.50% in problems of 100 cities and from 8.10-9.67% to 2.00-2.79% in issues of 200 cities.

Anahtar Kelimeler

• The bees algorithm
• Optimization
• The traveling salesman problem
• Variable multiple insertion
• neighborhood

Yerel Arama Bölümü Güncellenmiş Arı Algoritması ile Gezgin Satıcı Problemi Optimizasyonu

Öz

Klasik optimizasyon yöntemleri ile çok sayıda bağlantıya sahip gezgin satıcı problemlerinin çözülebilmesi zordur. Bu kapsamda, aramalarını optimum bir çözüme yönlendiren meta-sezgisel algoritmalar tercih edilmektedir. Bu çalışmada, bu meta-sezgisel algoritmalardan biri olan ve bal arılarının yiyecek arama yöntemlerinden esinlenerek geliştirilen Arı Algoritması incelenmiştir. Çalışmanın amacı, Arı Algoritmasının gezgin satıcı problemlerinin çözümüne yönelik etkinliğinin artırılmasıdır. Klasik Arı Algoritması içerisine Değişken Çoklu Ekleme operatörü eklenmiş ve yakın komşuluk bölgeleri içerisinde arama yapılarak, farklı gezgin satıcı problemleri için testler yapılmıştır. Yapılan testler sonucunda bu algoritma ile literatürdeki diğer Arı Algoritmalarına göre çok daha iyi sonuçlar elde edildiği görülmüştür. Geliştirilen algoritma ile 100 şehirlik problemlerde sapma değerleri %1,40-2,80 aralığından %0,11-0,50 aralığına ve 200 şehirlik problemlerde de %8,10-9,67 aralığından %2.00-2,79 aralığına indirildiği gözlemlenmiştir.

Anahtar Kelimeler

• Arı algoritması
• Değişken çoklu ekleme
• Gezgin satıcı problemi
• Komşuluk
• Optimizasyon

Destekleyen Kurum

Roketsan A. Ş.

Kaynakça

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