Çok amaçlı yapay alg algoritması ile kısıtlı mühendislik tasarım problemlerinin çözülmesi

Öz

Mühendislik tasarım problemleri, optimize edilmesi oldukça zor olan problemler sınıfına girer. Doğadan ilham alan metasezgisel teknikler, bu tür problemleri çözmek için faydalı olabilmektedir. Bu çalışmada, yakın zamanda önerilen çok amaçlı yapay alg algoritması (MOAAA) kullanılarak 7 tanesi benchmark problemi, 7 tanesi mühendislik tasarım problemi olmak üzere toplam 14 farklı problem optimize edilmiştir. MOAAA’nın performans testi için, HV, SPREAD, EPSILON ve IGD olarak isimlendirilen 4 farklı metrik kullanılmıştır. Performans karşılaştırması literatürde iyi bilinen NSGA-II, PAES, MOCell, IBEA ve MOVS algoritmaları ile yapılmıştır. Tüm algoritmalar için elde edilen metriklere Friedman testi uygulanmış ve her algoritmanın ortalama başarı sırası hesaplanmıştır. Sonuçlar, MOAAA'nın 4 performans metriğinden 3'ünde diğer algoritmalardan daha iyi performansa sahip olduğunu göstermektedir. Ayrıca Wilcoxon testi, MOAAA ile elde edilen sonuçların %95 güven düzeyinde anlamlı olduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Yapay alg algoritması
• Çok amaçlı kısıtlı optimizasyon
• Metasezgisel algoritmalar
• Çok amaçlı mühendisliktasarım problemleri

Solving constrained engineering design problems with multi-objective artificial algae algorithm

Abstract

Engineering design problems fall into the category of problems that are very difficult to optimize. Nature-inspired metaheuristic techniques can be beneficial to solve such problems. In this study, a total of 14 different problems, 7 of which are benchmark problems and 7 of which are engineering design problems, were optimized using the recently proposed multi-objective artificial algae algorithm, MOAAA for short. For the performance test of the MOAAA, 4 different metrics named HV, SPREAD, EPSILON and IGD were used. Performance comparison was made with NSGA-II, PAES, MOCell, IBEA and MOVS algorithms which are well known in the literature. The Friedman test was applied to the metrics obtained for all algorithms and the average ranks of each algorithm were calculated. The results show that MOAAA has better performance than other algorithms in 3 of 4 metrics. In addition, the Wilcoxon's test reveals that the results obtained by the MOAAA are significant in the 95% confidence level.

Keywords

• Artificial algae algorithm
• Multi-objective constrained optimization
• Metaheuristic algorithms
• Multi-objective engineering design problems

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