KOMBİNATORYAL ENİYİLEME PROBLEMLERİNİN ÇÖZÜMÜ İÇİN PARAMETRESİZ VE METAFORSUZ METASEZGİSEL ALGORİTMA ÖNERİSİ

Pek çok eniyileme problemi karmaşıktır ve çözülebilmesi için önemli miktarda hesaplama çabası gerektirmektedir. Söz konusu eniyileme problemleri araştırmacıların ilgisini çekmiş ve araştırmacılar bu problemlerin çözümünde kullanmak üzere birçok metasezgisel algoritma önermişlerdir. Geliştirilen metasezgisel algoritmaların çoğu metaforlara dayanmaktadır. Bu sebeple algoritmalar ilham alınan metaforların doğasını yansıtmak üzere parametre değerlerine sahiptirler. Bu durum algoritmanın sade olan yapısını bozmakta ve algoritmayı çalıştırmak için fazladan iş yükü getirmektedir. Ancak eniyileme problemleri sade, kullanışlı, metaforsuz ve parametresiz algoritmalarla da çözdürülebilir. Bu çalışmanın temel motivasyonu tam olarak söz konusu sade ve metaforsuz algoritma tasarımıdır. Bu çalışmada kombinatoryal eniyileme problemlerini çözmek için yeni bir metasezgisel yöntem olan Kesikli Rao Algoritması geliştirilmiştir. Kesikli Rao Algoritması (KRA) bilinen Rao algoritmasının bazı bileşenlerinde güncellemeler yapılarak elde edilmiştir. KRA’nın performansı iyi bilinen bir kombinatoryal eniyileme problemi olan Gezgin Satıcı Problemi (GSP) için değerlendirilmiştir. Literatürde yer alan farklı boyutlardaki test problemleri kullanılmıştır. Sonuç olarak, geliştirilen algoritma ile makul çözüm sürelerinde yüksek kaliteli çözümler elde edilmiştir ve geliştirilen algoritmanın GSP için literatürdeki diğer algoritmalarla yarışabilir nitelikte olduğu görülmüştür.

Anahtar Kelimeler:

Metasezgisel Algoritmalar, Rao Algoritması, Kesikli Eniyileme, Gezgin Satıcı Problemi

PARAMETER-LESS AND METAPHOR-LESS METAHEURISTIC ALGORITHM SUGGESTION FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS

Many optimization problems are complex, challenging and take a significant amount of computational effort to solve. These problems have gained the attention of researchers and they have developed lots of metaheuristic algorithms to use for solving these problems. Most of the developed metaheuristic algorithms are based on some metaphors. For this reason, these algorithms have algorithm-specific parameters to reflect the nature of the inspired metaphor. This violates the algorithm's simplicity and brings extra workload to execute the algorithm. However, the optimization problems can also be solved with simple, useful, metaphor-less and algorithm-specific parameter-less metaheuristic algorithms. So, it is the essential motivation behind this study. We present a novel metaheuristic algorithm called Discrete Rao Algorithm (DRA) by updating some components of the generic Rao algorithm to solve the combinatorial optimization problems. To evaluate the performance of the DRA, we perform experiments on Traveling Salesman Problem (TSP) which is the well-known combinatorial optimization problem. The experiments are performed on different sized benchmark problems in the literature. The computational results show that the developed algorithm has obtained high quality solutions in a reasonable computation time and it is competitive with other algorithms in the literature for solving the TSP.

Keywords:

Metaheuristic Algorithms, Rao Algorithm, Discrete Optimization, Traveling Salesman Problem,

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