Yeni önerilmiş ikili fare sürüsü algoritması

Son zamanlarda araştırmacılar, basit yapısı, yüksek optimizasyon performansı ve adaptasyon kolaylığı nedeniyle optimizasyonda sürü tabanlı algoritmalara ilgi duymaya başlamışlardır. Sürü tabanlı algoritmalar, her ne kadar sürekli optimizasyon problemlerini çözmek için kullanılsalar da ikili optimizasyon problemlerini çözmek için de kullanılabilirler. Sürekli optimizasyonda arama uzayı değişkenleri optimal değere yaklaşmaya çalışırken, ayrık optimizasyonda arama uzayı değişkenleri sabittir ve gerçek değerlerle ifade edilir. İkili optimizasyon ise, karar değişkenleri 0 ve 1 değerleri alır ve temel olarak ayrık optimizasyon sınıfında yer alır. Bu makalede sürekli optimizasyon problemlerini çözmek için önerilmiş Fare Sürüsü Algoritması (FSA) incelenmiştir. FSA, sürü zekâsına dayalı bir algoritmadır. Farelerin kovalama ve saldırma davranışları taklit edilerek FSA geliştirilmiştir. Bu çalışmada, orijinal FSA, ikili optimizasyon problemlerini çözmek için tekrar güncellenmiştir ve İkili FSA (BinFSA) önerilmiştir. BinFSA’da sürekli arama alanı değerlerini ikili değerlere dönüştürürken dört adet U ve dört adet T şekilli transfer işlevi kullanılmıştır. Böylece BinFSA'nın sekiz varyantı elde edilmiştir. Bunlar BinFSA1, BinFSA2, BinFSA3, BinFSA4, BinFSA5, BinFSA6, BinFSA7 ve BinFSA8 şeklinde isimlendirilmişlerdir. Bu varyantlar içinden BinFSA'nın en başarılı varyantı BinFSA6 olarak belirlenmiştir. Daha sonra BinFSA6 varyantı, çaprazlama ve mutasyon operatörleri eklenerek geliştirilmiştir ve GBinFSA olarak adlandırılmıştır. GBinFSA’nın performansı sırt çantası problemlerinde test edilmiştir. Ayrıca GBinFSA'nın başarısı literatürden seçilen farklı sezgisel algoritmalarla da karşılaştırılmıştır. Elde edilen sonuçlara göre önerilen algoritmanın çözüm kalitesinin etkili ve karşılaştırılabilir olduğu görülmüştür. Sonuçlar, GBinFSA'nın ikili optimizasyon problemleri için tercih edilen bir buluşsal algoritma olduğunu göstermiştir.

Anahtar Kelimeler:

FSA, İkili optimizasyon, Fare, Çaprazlama, Mutasyon

The new suggested binary rat swarm algorithm

Recently, researchers have started to be interested in population-based swarm-based algorithms in optimization due to their simple structure, high optimization performance, and ease of adaptation. Although swarm-based algorithms solve continuous optimization problems, they can also be used to solve binary optimization problems. In continuous optimization, the search space variables try to approach the optimal value, while in discrete optimization, the search space variables are fixed and expressed with real values. In binary optimization, the decision variables take values of 0 and 1 and are basically in the discrete optimization class. In this paper, the proposed Rat Swarm Algorithm (RSA) to solve continuous optimization problems is examined. RSA is an algorithm based on swarm intelligence. RSA was developed by imitating the chasing and attacking behaviors of rats. In this study, the original RSA was updated again to solve binary optimization problems and Binary RSA (BinRSA) was proposed. In BinRSA, four U-shaped and four T-shaped transfer functions are used while converting the continuous search field values to binary values. Thus, eight variants of BinRSA were obtained. These are named as BinRSA1, BinRSA2, BinRSA3, BinRSA4, BinRSA5, BinRSA6, BinRSA7 and BinRSA8. Among these variants, the most successful variant of BinRSA was determined as BinRSA6. Then the BinRSA6 variant was developed by adding crossover and mutation operators and was named GBinRSA. GBinRSA's performance has been tested in knapsack problems. In addition, the success of GBinRSA was compared with different heuristic algorithms selected from the literature. According to the results obtained, it has been seen that the solution quality of the proposed algorithm is effective and comparable. The results showed that GBinRSA is a preferred heuristic for binary optimization problems.

Keywords:

RSA, Binary optimization, Rat, Crossover, Mutation,

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