Kapasitesiz tesis yerleşim problemleri için geliştirilmiş ikili güve alevi optimizasyon algoritması

Öz

Güve Alevi Optimizasyonu, sürekli gerçek dünya problemlerini çözmek için doğadan ilham alan bir meta-sezgisel algoritmadır. Bu çalışmada, kapasitesiz tesis yerleşim problemlerini çözmek için ikili Enhanced MFO Desert Bush (binEMFO-DB) algoritması olarak adlandırılan MFO'nun değiştirilmiş bir versiyonu önerilmiştir. Önerilen algoritma üç değişiklik içermektedir: i) kaotik harita tabanlı popülasyon başlatma, ii) rastgele alev seçimi ve iii) çöl çalısı stratejisi. Önerilen binEMFO-DB algoritmasının performansı, OR-Library'den alınan 15 farklı UFL problemi üzerinde test edilmiş ve parametre analizi için Taguchi ortogonal dizi tasarımı kullanılmıştır. Algoritmalar ile elde edilen sonuçların ortalama, boşluk ve isabet değerleri performans metriği olarak kullanılmıştır. binEMFO-DB'nin performansı, son teknoloji algoritmaların performanslarıyla karşılaştırılmıştır. Elde edilen sonuçlar, önerilen binEMFO-DB'nin test ortamında başarılı ve rekabetçi bir performansa sahip olduğunu göstermektedir.

Anahtar Kelimeler

• Güve alevi optimizasyonu
• Kapasitesiz tesis yerleşim problemi
• İkili optimizasyon
• Çöl çalısı
• Transfer fonksiyonları
• Taguchi

A binary enhanced moth flame optimization algorithm for uncapacitated facility location problems

Abstract

Moth Flame Optimization is a nature-inspired meta-heuristic algorithm for constantly solving real-world problems. In this study, a modified version of MFO called binary Enhanced MFO Desert Bush (binEMFO-DB) algorithm is proposed to solve uncapacitated facility location problems. The proposed algorithm includes three modifications: i) chaotic mapbased population initialization, ii) random flame selection, and iii) desert bush strategy. The performance of the proposed binEMFO-DB algorithm was tested on 15 different UFL problems from the OR-Library and Taguchi orthogonal array design was used for parameter analysis. The average, gap and hit values of the results obtained by the algorithms were used as performance metrics. The performance of binEMFO-DB is compared with the performance of state-of-the-art algorithms. The results show that the proposed binEMFO-DB has a successful and competitive performance in the test environment.

Keywords

• Moth flame optimization
• Uncapacitated facility location problem
• Binary optimization
• Desert bush
• Transfer functions
• Taguchi

Kaynakça

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