Farklı Donör-Alıcı Konfigürasyonları ile İmmün Plazma Algoritmasının Performans Analizi

Öz

İmmün Plazma Algoritması (IPA), immün plazma transfer tedavisinden ilham alan yeni bir meta-sezgisel algoritmadır. Karmaşık optimizasyon problemlerini çözmek için birçok meta-sezgisel algoritma kullanılır, ancak performansları çoğunlukla 30 boyutlu problemlerde denetlenmiştir. Günümüzde 50'den fazla boyutlu yüksek boyutlu optimizasyon problemlerinin çözülmesini gerektiren çok daha karmaşık sistemlerle uğraşıyoruz, ancak bu meta-sezgisel algoritmaların yüksek boyutlu problemler için performansı çoğunlukla incelenmemiştir. Bu problemin üstesinden gelmek için bu çalışmada IPA'nın yüksek boyutlu problemlerin çözümündeki performansı araştırılmıştır. Bu durumda, 100 boyutlu beş iyi bilinen kıyaslama optimizasyon problemini (Sphere, Quartic, Rastrigin, Ackley ve Griewank fonksiyonları) çözmek için IPA kullanılmıştır. Devamında, bazı son teknoloji meta-sezgisel algoritmalarla karşılaştırılmıştır. Deneysel sonuçlar, IPA'nın en iyi amaç değerlerini bulmada, bu algoritmalardan daha iyi performans gösterdiğini ve test edilen optimizasyon problemlerinin çoğu için en iyi standart sapmaya ve en iyi ortalama değere sahip olduğunu göstermektedir.

Anahtar Kelimeler

• Yüksek boyutlu Problemler
• Optimizasyon
• İmmün Plazma Algoritması

Performance Analysis of Immune Plasma Algorithm with Different Donor-Receiver Configurations

Abstract

Immune Plasma Algorithm (IPA) is a novel meta-heuristic algorithm inspired by immune plasma transfer treatment. Many meta-heuristic algorithms are used for solving complex optimization problems, but their performance is mostly inspected on problems with 30 dimensions. Nowadays we are dealing with far more complex systems that require solving high-dimensional optimization problems with over 50 dimensions whereas performance of meta-heuristic algorithms for high-dimensional problems is mostly unexamined. So to overcome this problem, in this study, performance of IPA on solving high-dimensional problems is investigated. In this case, it is used to solve five well-known benchmark optimization problems with 100 dimensions. In this work, Immune Plasma Algorithm (IPA) is used for solving Sphere, Quartic, Rastrigin, Ackley and Griewank functions. It is compared with some other state-of-the-art meta-heuristic algorithms. Experimental results demonstrate that IPA outperforms these algorithms in finding best objective values, and has best standard deviation, and best mean value for most of the tested optimization problems.

Keywords

• High-dimensional Problems
• Optimization
• Immune Plasma Algorithm

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