Hiperküre Dinamikleriyle Geliştirilmiş Parçacık Sürü Optimizasyonu Kullanılarak Dişli Tasarımı Problemi Çözümleri

Abstract

Bu çalışma, küresel optimizasyon problemlerinin çözümünde yaygın olarak kullanılan bir optimizasyon algoritması olan Parçacık Sürü Optimizasyonu'nun (PSO) gelişmiş bir versiyonunu sunmaktadır. Hiperküre Dinamikleri ve Mutasyonlu PSO (PSO-HDM) olarak adlandırılan bu yöntem, optimizasyon engelleriyle başa çıkmak için geliştirilmiştir. Yeni yöntem, parçacıkların konumlarının hiperkürelerin dönüşü kullanılarak güncellendiği yenilikçi bir teknik kullanmakta ve bu sayede arama uzayının daha iyi keşfedilmesini sağlamaktadır. Buna ek olarak, Jitter ve Gaussian adında iki yeni mutasyon tekniği, yerel optimumlardan kaçınmak ve çözüm çeşitliliğini artırmak için uygulanmaktadır. Klasik PSO'nun bilişsel ve sosyal katsayılar gibi parametrelerinin dinamik olarak değiştirilmesi, algoritmanın başarımını daha da artırmaktadır. PSO-HDM optimizasyon algoritması, bazı temel test fonksiyonları kullanılarak değerlendirilmiş ve klasik PSO ile karşılaştırıldığında, optimum çözümleri belirlemede daha iyi sonuçlar elde etmiştir. Yeni önerilen yöntemin etkinliğini göstermek için, bir mühendislik tasarım problemi olarak dişli treni tasarımı problemi seçilmiştir. Elde edilen sonuçlar, önerilen yöntemin yetkinliğini göstermektedir. Bu önerilen optimizasyon algoritması, literatürde önerilen diğer optimizasyon algoritmalarına bir alternatif olarak görülebilir.

Keywords

• Optimizasyon
• Meta-sezgisel algoritmalar
• Parçacık Sürü Optimizasyonu
• Mutasyon
• Hiperküre
• Dişli tasarımı problemi

Gear Train Design Solutions via Hypersphere Dynamics Driven Advanced Particle Swarm Optimization

Abstract

This work presents an advanced version of the Particle Swarm Optimization (PSO) algorithm, a well-known optimization algorithm for the solution of the global optimization problems, called PSO with Hypersphere Dynamics and Mutation (PSO-HDM), to deal with the optimization obstacles. The novel method employs a novel technique where the particles’ positions are updated using the rotation of the hyperspheres, providing for better exploration of the search space. In addition, two new mutation techniques, Jitter and Gaussian, are used to keep away from the local optima and enhance the solution variety. Dynamic modifications of the classical PSO’s parameters, such as cognitive and social coefficients, also improve the algorithm’s achievement. The PSO-HDM optimization algorithm is evaluated with utilizing some benchmark functions and compared to classical PSO, getting better values in determining the optimal solutions. Gear train design problems are selected as an engineering design problem to show the effectiveness of the new suggested method. The obtained results present the capability of the proposed method. This proposed optimization algorithm could be seen as an alternative method to other optimization algorithms proposed in the literature.

Keywords

• Optimization
• Metaheuristic algorithms
• Particle Swarm Optimization
• Mutation
• Hypersphere
• Gear train design problem.

Ethical Statement

The study is complied with research and publication ethics.

Thanks

We would like to thank Assoc. Prof. Dr. Korhan Günel for his valuable contributions.

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