Hamming Mesafesi ile Lokal Arama Tabanlı İkili Yapay Arı Kolonisi Algoritması

Year 2020, Volume: 24 Issue: 1, 120 - 131, 20.04.2020

Zeynep Banu Özger , Bülent Bolat , Banu Diri

https://doi.org/10.19113/sdufenbed.635465

Abstract

Yapay Arı Kolonisi Algoritması sürekli uzay problemleri için geliştirilen, popülasyon tabanlı, doğadan esinlemeli bir optimizasyon algoritmasıdır. Bu çalışmanın amacı, büyük veride, öznitelik alt küme seçimi problemini efektif bir biçimde çözmek için Yapay Arı Koloni (YAK) Algoritmasının ikili bir versiyonunu geliştirmektir. YAK Algoritması başarılı bir global yakınsama sunmakla birlikte lokal bölgedeki olası çözümleri gözden kaçırabilmektedir. Algoritmanın komşu kaynak seçimi mekanizmasına, Hamming Mesafe ölçümü tabanlı bir yerel arama prosedürü eklenmiştir. Ayrıca, yeniden nüfus stratejisi ile popülasyonun çeşitliliği artırılmış ve erken yakınsama önlenmiştir. UCI Makine Öğrenmesi Havuzu’ndan, öznitelik sayısı 100’den fazla olan 14 veri kümesi seçilmiş ve önerilen yöntem ile öznitelik seçimi yapılmıştır. Algoritmanın performansı, yaygın kullanılan ve başarısı kanıtlanmış üç sezgisel algoritma ile sınıflandırma hatası, seçilen öznitelik sayısı ve hesapsal maliyet bakımından karşılaştırılmıştır. Elde edilen sonuçlar, YAK algoritmasına entegre edilen lokal arama prosedürünün, algoritmanın performansını tüm kriterler bakımından artırdığını göstermektedir. 

Keywords

Yapay arı kolonisi, Veri madenciliği, Sezgisel Algoritmalar, Makine Öğrenmesi

A Locally Searched Binary Artificial Bee Colony Algorithm Based on Hamming Distance for Binary Optimization

Abstract

Artificial Bee Colony is a population based, bio-inspired optimization algorithm that developed for continues problems. The aim of this study is to develop a binary version of the Artificial Bee Colony (ABC) Algorithm to solve feature subset selection problem on bigger data. ABC Algorithm, has good global search capability but there is a lack of local search in the algorithm. To overcome this problem, the neighbor selection mechanism in the employed bee phase is improved by changing the new source generation formula that has hamming distance based local search capacity. With a re-population strategy, the diversity of the population is increased and premature convergence is prevented. To measure the effectiveness of the proposed algorithm, fourteen datasets which have more than 100 features were selected from UCI Machine Learning Repository and processed by the proposed algorithm. The performance of the proposed algorithm was compared to three well-known algorithms in terms of classification error, feature size and computation time. The results proved that the increased local search ability improves the performance of the algorithm for all criteria.

Keywords

Artificial bee colony, Data mining, Heuristic algorithms, Machine learning

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