Multiple travelling salesman problem with fuzzy c-means and ant colony optimization algorithms

Abstract

Sezgisel algoritmalar, kabul edilebilir sürede optimuma yakın çözümler verebilen ve çok büyük boyutlu optimizasyon problemleri için kullanılabilen algoritmalardır. En iyi çözümün bulunacağı garanti edilememekle beraber, bulunan çözümün kabul edilebilir düzeyde olması, çözüme kolay ve hızlı ulaşılabilmesi açısından kullanımı oldukça yaygın olan yöntemlerdir. Sezgisel yöntemlerde problemin çözümüne yönelik yaklaşımlar; karar verme, optimizasyon, bulanık mantık, yapay zeka, makine öğrenmesi, derin öğrenme şeklinde karşımıza çıkar. Bu çalışmada; pek çok alanda uygulaması olan Gezgin Satıcı Problemi (GSP) için optimuma en yakın çözümü hızlı bir şekilde bulabilmek amacıyla Karınca Kolonisi Optimizasyonu (KKO) yöntemi seçilmiştir. Rastgele seçilen verileri gruplandırmak amacıyla da Bulanık C-Ortalamalı Kümeleme (BCO) Algoritması kullanılmıştır. Çalışmada kullanılan veriler BCO algoritması kullanılarak ayrı ayrı 3, 4 ve 5 kümeye ayrılmış; elde edilen veri setleri Çoklu KKO ile değerlendirilmiş ve sonuçlar karşılaştırılmıştır.

Keywords

• Karınca kolonisi optimizasyonu
• Bulanık c-ortalamalı kümeleme
• Çoklu gezgin satıcı problemi

Multiple travelling salesman problem with fuzzy c-means and ant colony optimization algorithms

Abstract

Heuristic algorithms are algorithms that can provide near-optimum solutions for very large-scale optimization problems in an acceptable time. They are commonly used methods in terms of being at an acceptable level and reaching a solution easily and quickly, although it cannot be guaranteed that the best solution will be found. Problem solving approaches being used in heuristic methods; decision making, optimization, fuzzy logic, artificial intelligence, machine learning, deep learning. In this paper; chosen method in order to find the best solution of route optimization for the Travelling Salesman Problem (TSP), which has applications in many areas, is that Ant Colony Optimization (ACO). For classification of the data, that is randomly selected, Fuzzy C-Mean Clustering (FMC) Algorithm has being used. The data set has being separated into 3, 4 and 5 clusters and all cluster data sets obtained were evaluated with Multiple ACO and then the results were compared.

Keywords

• Ant colony optimization
• Fuzzy c-means clustering
• Multiple travelling salesman problem

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