ÇOK AMAÇLI MÜHENDİSLİK TASARIMI VE KISITLI PROBLEMLER İÇİN HİBRİT BİRÇOK AMAÇLI OPTİMİZASYON ALGORİTMASI

Year 2021, Volume: 9 Issue: 4, 1200 - 1211, 20.12.2021

Murat Karakoyun Halife Kodaz

https://doi.org/10.21923/jesd.930887

Cited By: 1

Abstract

Gerçek dünya problemlerine bakıldığında çoğunun birden fazla hedefi gerçekleştirmeye yönelik olduğu görülmektedir. Bu problemlerin çözümü için kullanılan birçok klasik yöntem mevcuttur. Klasik yöntemlerin çözüm geliştirme noktasında farklı sebeplerden dolayı eksik kalması araştırmacıları farklı yaklaşımlar geliştirmeye yöneltmiştir. Genellikle doğada sürü halinde yaşayan hayvanların veya farklı yaşam alanlarına sahip bitkilerin davranışlarından esinlenilerek geliştirilen doğa esinli algoritmalar bu yaklaşımlardan bir tanesi olmuştur. Bu çalışmada, tek amaçlı problemlerin çözümü için geliştirilmiş olan kurbağa sıçrama (SFLA) ve gri kurt optimizasyonu (GWO) algoritmaları hibrit bir şekilde kullanılarak çok amaçlı optimizasyon problemlerine uygulanmıştır. Önerilen algoritma bazı çok amaçlı mühendislik tasarımı ve çok amaçlı kısıtlı problemlerin üzerinde uygulanmıştır. Önerilen algoritmanın performansı NSGA-II, IBEA, MOCell ve PAES algoritmalarının performansı ile kıyaslanmıştır. Performans karşılaştırma metriği olarak HV, IGD, Spread ve Epsilon metrikleri kullanılmıştır. Performans analizi; elde edilen ortalama sonuçlar, Friedman sıralama testi ve Wilcoxon anlamlılık testi ile yapılmıştır. Deneysel sonuçlar, önerilen algoritmanın diğer algoritmalardan daha başarılı sonuçlar ürettiğini göstermiştir.

Keywords

Çok Amaçlı Optimizasyon, Gri Kurt Optimizasyonu, Mühendislik Tasarımı, Pareto Teoremi.

A HYBRID MULTI OBJECTIVE OPTIMIZATION ALGORITHM FOR MULTI OBJECTIVE ENGINEERING DESIGN AND CONSTRAINED PROBLEMS

Abstract

When looking to the real-world problems, it is seen that many of them are aimed at achieving more than one goal. The deficiency of the classical methods at the point of developing solutions due to different reasons has led researchers to develop different approaches. Nature-inspired algorithms developed by taking inspiration from the behavior of animals that generally live with a swarm in nature or plants with different habitats have been one of these approaches. In this study, shuffled frog leaping (SFLA) and gray wolf optimizer (GWO) algorithms, which developed for solving single-objective problems, are applied to multi-objective optimization problems in a hybrid manner. The proposed algorithm has been applied on some multi-objective engineering design and multi-objective constrained problems. The performance of the proposed algorithm has been compared with the performance of NSGA-II, IBEA, MOCell and PAES algorithms. HV, IGD, Spread and Epsilon metrics are used as performance comparison metrics. Performance analysis was performed using the average results obtained, Friedman ranking test and Wilcoxon significance test. Experimental results have shown that the proposed algorithm generates more successful results than other algorithms.

Keywords

Multi-Objective Optimization, Grey Wolf Optimizer, Engineering Design, Pareto Theorem.

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