A Systematic Circular Weight Initialisation of Kohonen Neural Network for Travelling Salesman Problem

Self-organising neural networks have since been employed by researchers in solving the travelling salesman problem. However, with these networks, the final tour length as well as the convergence time, largely depends on the initial weights of the networks. In this paper, systematic initialisation of Kohonen neural network weight in a circle is presented, that involves randomly initialising the weights to be along a circular path, with centroid equals the centroid of all the cities. Our major contribution is on having the circle exhibit different radius on each test run, in order to effectively encompass all the cities. This will increase the chance of attaining the global solution by preventing effect of local minima due to some cities that may be poorly covered by the circle having specific radius. Furthermore, a system of determining the most efficient number of neurons needed in the circle is devised. It was experimentally found that, having a circle of size 1.5 times the number of cities gives the best performance. Results generated indicate an average deviation error of 2.74% from the optimal solutions of 13 TSPLIB benchmark TSP instances. © 2016 IEEE.

  • Koleksiyonlar
Erişime Açık
Görüntülenme
7
22.03.2024 tarihinden bu yana
İndirme
1
22.03.2024 tarihinden bu yana
Son Erişim Tarihi
18 Mayıs 2024 13:34
Google Kontrol
Tıklayınız
Tam Metin
Tam Metin İndirmek için tıklayın Ön izleme
Detaylı Görünüm
Eser Adı
(dc.title)
A Systematic Circular Weight Initialisation of Kohonen Neural Network for Travelling Salesman Problem
Yayın Türü
(dc.type)
Konferans Bildirisi
Yazar/lar
(dc.contributor.author)
MUHAMMED, Jawad
Yazar/lar
(dc.contributor.author)
ALTUN, Halis
DOI Numarası
(dc.identifier.doi)
10.1109/SIU.2016.7495977
Atıf Dizini
(dc.source.database)
Scopus
Konu Başlıkları
(dc.subject)
Travelling Salesman Problem
Konu Başlıkları
(dc.subject)
Weight İnitilization
Konu Başlıkları
(dc.subject)
Kohonen Artificial Neural Network
Yayıncı
(dc.publisher)
Institute of Electrical and Electronics Engineers Inc.
Yayın Tarihi
(dc.date.issued)
2016
Kayıt Giriş Tarihi
(dc.date.accessioned)
2020-08-07T12:56:45Z
Açık Erişim tarihi
(dc.date.available)
2020-08-07T12:56:45Z
Kaynak
(dc.source)
24th Signal Processing and Communication Application Conference, SIU 2016
ISSN
(dc.identifier.issn)
9781509016792 (ISBN)
Özet
(dc.description.abstract)
Self-organising neural networks have since been employed by researchers in solving the travelling salesman problem. However, with these networks, the final tour length as well as the convergence time, largely depends on the initial weights of the networks. In this paper, systematic initialisation of Kohonen neural network weight in a circle is presented, that involves randomly initialising the weights to be along a circular path, with centroid equals the centroid of all the cities. Our major contribution is on having the circle exhibit different radius on each test run, in order to effectively encompass all the cities. This will increase the chance of attaining the global solution by preventing effect of local minima due to some cities that may be poorly covered by the circle having specific radius. Furthermore, a system of determining the most efficient number of neurons needed in the circle is devised. It was experimentally found that, having a circle of size 1.5 times the number of cities gives the best performance. Results generated indicate an average deviation error of 2.74% from the optimal solutions of 13 TSPLIB benchmark TSP instances. © 2016 IEEE.
Yayın Dili
(dc.language.iso)
tr
Tek Biçim Adres
(dc.identifier.uri)
http://hdl.handle.net/20.500.12498/3014
Analizler
Yayın Görüntülenme
Yayın Görüntülenme
Erişilen ülkeler
Erişilen şehirler
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve cerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms