Moleküler Potansiyel Enerji Fonksiyonu İçin Geliştirilmiş Sosyal Örümcek Algoritması

Abstract

Sosyal örümcek algoritması (SÖA), sürekli optimizasyon problemlerini çözmek için örümcek davranışları üzerine oluşturulan yeni bir sezgisel algoritmadır. Bu çalışmada, SÖA molekülün enerji fonksiyonunun basitleştirilmiş bir modelini en aza indirmek için kullanılmıştır. Moleküler potansiyel enerji fonksiyonu problemi, en önemli gerçek hayat problemlerinden biridir. Moleküler potansiyel enerji fonksiyonu problemi, bir proteinin 3D yapısını tahmin etmeye çalışır. Sosyal örümcek algoritması çeşitli teknikler (Çaprazlama-mutasyon ve Gbest yakınsaması-sessiz örümcek teknikleri) eklenerek geliştirilmiştir ve çeşitli tekniklerle geliştirilen SÖA 'ya Geliştirilmiş SSA (GSÖA) denilmiştir. Bu teknikler sayesinde, SÖA 'nın sürekli arama uzayında keşif ve sömürü yetenekleri geliştirilmiştir. SÖA ve GSÖA 'nın genel performansları, düşük ölçekli ve yüksek ölçekli on üç kıyaslama fonksiyonunda test edilmiştir ve elde edilen sonuçlar birbiriyle karşılaştırılmıştır. Wilcoxon işaretli testi, elde edilen SÖA ve GSÖA sonuçlarına uygulanmıştır. Daha sonra, SÖA ve GSÖA'nın genel performansı, farklı boyutlarda tanımlanan molekülün basitleştirilmiş bir modeli üzerinde test edilmiştir. Ayrıca, GSÖA'nın performansı, literatürdeki çeşitli sanatsal algoritmalarla da karşılaştırılmıştır. Sonuçlar, GSÖA 'nın performansının üstünlüğünü göstermiştir.

Keywords

• Sınırlı Optimizasyon
• Sosyal örümcek algoritması
• Moleküler enerji fonksiyonu

IMPROVED SOCIAL SPIDER ALGORITHM FOR MINIMIZING MOLECULAR POTENTIAL ENERGY FUNCTION

Abstract

The social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors to solve continuous optimization problems. In this study, SSA is used in order to minimize a simplified model of the energy function of the molecule. The Molecular potential energy function problem is one of the most important real-life problems. The Molecular potential energy function problem attempts to predict the 3D structure of a protein. SSA is developed by various techniques (Crossover-mutation and Gbest convergence-silent spider techniques) and SSA is called Improved SSA (ISSA). By these techniques, the exploration and exploitation capabilities of SSA in the continuous search space are improved. The general performances of SSA and ISSA are tested on low-scaled and large-scaled thirteen benchmark functions and obtained results are compared with each other. Wilcoxon signed-rank test is applied to SSA and ISSA results. Then, the general performance of the SSA and ISSA is tested on a simplified model of the molecule for different dimensions. Also, the performance of the ISSA is compared to various state-of-art algorithms in the literature. The results showed the superiority of the performance of ISSA.

Keywords

• Constrained Optimization
• Social spider algorithm
• Molecular energy function

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