An enhanced version of honey badger algorithm for data clustering problems

Öz

This study proposes an improved version of the Honey Badger Algorithm (HBA) for solving clustering problems, called the Clustering Honey Badger Algorithm (CHBA). The main enhancement involves modeling the smell intensity using an exponential decay function instead of the inverse square law. This modification reduces the likelihood of getting trapped in local optima and improves the algorithm’s exploratory behavior. CHBA was compared against six state-of-the-art meta-heuristic algorithms, including the original HBA, on seven benchmark clustering datasets. The evaluation was based on five common external performance metrics: accuracy, F-score, precision, sensitivity, and intra-cluster distance. According to the results, CHBA achieved the highest performance on datasets such as Cancer (94.86% accuracy), Iris (93.94% accuracy), and Ecoli (84.52% accuracy). Furthermore, Friedman test results showed that CHBA consistently ranked first in all performance metrics, with p-values less than 0.005, indicating statistically significant superiority. These findings demonstrate that CHBA is a competitive and reliable clustering algorithm, especially in complex and imbalanced data scenarios.

Anahtar Kelimeler

• Honey badger algorithm
• Clustering problem
• MetaHeuristic algorithm
• Swarm intelligence.

Veri kümeleme problemleri için bal porsuğu algoritmasının geliştirilmiş bir sürümü

Öz

Bu çalışma, kümeleme problemlerinin çözümüne yönelik olarak Bal Porsuğu Algoritmasının (HBA) geliştirilmiş bir versiyonu olan Kümeleme Bal Porsuğu Algoritması (CHBA)’yı önermektedir. Yapılan temel iyileştirme, avın koku yoğunluğunu modellemek için kullanılan ters kare yasası yerine eksponansiyel azalma fonksiyonunun uygulanmasıdır. Bu sayede algoritmanın yerel minimumlara takılma olasılığı azaltılmış ve keşif yeteneği artırılmıştır. CHBA, yedi farklı kümeleme veri kümesi üzerinde, orijinal HBA dahil altı güncel metasezgisel algoritma ile karşılaştırılmıştır. Karşılaştırmalar doğruluk, Fskor, keskinlik, duyarlılık ve küme içi mesafe olmak üzere beş yaygın dış performans metriğine göre yapılmıştır. Elde edilen sonuçlara göre, CHBA, özellikle Cancer (%94,86 doğruluk), Iris (%93,94 doğruluk) ve Ecoli (%84,52 doğruluk) veri kümelerinde en yüksek başarıyı göstermiştir. Ayrıca, tüm performans metrikleri için yapılan Friedman testinde CHBA’nın ortalama sıralama değeri en düşük algoritma olduğu ve p-değerlerinin tümünde <0.005 olduğu görülmüştür. Bu bulgular, CHBA’nın karmaşık ve dengesiz veri kümelerinde kullanılabilecek rekabetçi ve güvenilir bir kümeleme algoritması olduğunu göstermektedir.

Anahtar Kelimeler

• Bal porsuğu algoritması
• Kümeleme problemleri
• Meta sezgisel algoritmalar
• Sürü zekâsı.

Kaynakça

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