Çok amaçlı genetik algoritma için yeni bir seçim stratejisi önerisi: MultiMoora Rank seçimi

Genetik algoritmalar çözümü zor problemler için kabul edilebilir süre ve kalitede çözüm bulan metasezgisel bir tekniktir. Genetik algoritma uygulamalarında tercih edilen seçim stratejileri, çözüm kalitesini önemli ölçüde etkilemektedir. Bu çalışmada, Çok Amaçlı Genetik Algoritmalar (ÇAGA)’ın performansını arttırmak amacıyla, çok kriterli karar verme yöntemlerinden biri olan MultiMoora metoduna dayalı MultiMoora Rank Seçimi (MMRS) seçim stratejisi geliştirilmiştir. Geliştirilen metodun performansı çok amaçlı akış tipi çizelgeleme problemlerinde test edilmiştir. MultiMoora Rank Seçimi ile elde edilen sonuçlar, genetik algoritmada yaygın olarak kullanılan Rulet Tekerleği Seçimi, Lineer Rank Seçimi ve Turnuva Seçimi metotlarının aynı problem üzerindeki sonuçları ile karşılaştırılarak değerlendirilmiştir. Elde edilen sonuçlar, önerilen MultiMoora Rank Seçimi metodunun karşılaştırılan diğer metotlara üstünlük sağladığını göstermektedir.

Anahtar Kelimeler:

Çok amaçlı optimizasyon, akış tipi çizelgeleme, genetik algoritma, MultiMoora, seçim metotları

A new selection strategy for multi objective genetic algorithm: MultiMoora Rank Selection

Genetic algorithms are metaheuristic methods that provide satisfactory solutions for complex problems within acceptable time periods. In genetic algorithms, the selection strategy considerably affects the quality of the solution. In the study, the MultiMoora Rank Selection (MMRS) strategy based on the MultiMoora method, a multi-criteria decision-making method, was developed to improve the performance of the Multi Objective Genetic Algorithms (MOGA). The performance of the method was tested using flow-shop scheduling problems. The results obtained with the MultiMoora Rank Selection were evaluated by comparing with the results obtained using the same problem with the selection methods that are widely used for genetic algorithms, such as the Roulette Wheel Selection, Linear Rank Selection and Tournament Selection methods. The results showed that the proposed MultiMoora Rank Selection method outperformed the compared methods.

Keywords:

Multi objective optimization, flow shop scheduling, genetic algorithm, MultiMoora, selection methods,

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