Comparison of Current Metaheuristic Optimization Algorithms by Using Benchmark Functions

Abstract

There are dozens of metaheuristic optimization methods inspired by the swarm behaviors of creatures in nature, plants, human-specific phenomena, events in scientific fields such as physics, mathematics, biology and chemistry. These methods are successful in certain problems but not in all problems. Therefore, new metaheuristic methods are suggested by researchers every day. In this study, for the first time, six up-to-date metaheuristic optimization algorithms, namely Jellyfish Search Optimizer, Carnivorous Plant Algorithm, Giza Pyramids Construction Algorithm, Gradient Based Optimizer, Student Based Psychology Optimization and Tunicate Swarm Optimization, were compared by using 10 mathematical benchmark functions with 10, 30 and 50 dimensions. According to the results obtained, Student Based Psychology Optimization gave the best results in 7 out of 10 benchmark functions. It was observed that Gradient Based Optimizer gave the best results in 4 benchmark functions. Carnivorous Plant Algorithm and Tunicate Swarm Optimization showed the worst performance. In order to compare in terms of time, algorithms were run at 1000 iterations in 50-dimensional benchmark functions and when the average run times obtained are examined, it is seen that Jellyfish Search Optimizer and Tunicate Swarm Optimization are the fastest running algorithms. Carnivorous Plant Algorithm and Student Based Psychology Optimization were the slowest running algorithms.

Keywords

• Benchmark Functions
• Global Optimization
• Metaheuristic Algorithms

Kalite Test Fonksiyonları Kullanılarak Güncel Metasezgisel Optimizasyon Algoritmalarının Karşılaştırılması

Abstract

Doğadaki canlıların sürü davranışlarından, bitkilerden, insana özgü olgulardan, fizik, matematik, biyoloji ve kimya gibi bilimsel alanlardaki olaylardan esinlenen onlarca metasezgisel optimizasyon yöntemi mevcuttur. Bu yöntemler belirli problemlerde başarılı olmakla birlikte bütün problemlerde başarılı olamamaktadır. Bundan dolayı araştırmacılar tarafından her geçen gün yeni metasezgisel yöntemler önerilmektedir. Bu çalışmada ilk defa güncel Yapay Deniz Anası Optimizasyonu, Etçil Bitki Optimizasyonu, Giza Piramitleri İnşaatı Optimizasyonu, Gradyan Tabanlı Optimizasyon, Öğrenci Psikolojisine Dayalı Optimizasyon ve Tunik Sürüsü Optimizasyonu olmak üzere altı güncel metasezgisel optimizasyon algoritması 10 adet matematiksel kalite testi foksiyonunda 10, 30 ve 50 boyut değerleri baz alınarak ayrıntılı bir şekilde karşılaştırılmıştır. Elde edilen sonuçlara göre 10 kalite testinden 7’sinde en iyi sonuçları Öğrenci Psikolojisine Dayalı Optimizasyon vermiştir. Gradyan Tabanlı Optimizasyon’un ise 4 kalite testinde en iyi sonuçları verdiği görülmüştür. En kötü performansı ise Etçil Bitki Optimizasyonu ve Tunik Sürüsü Optimizasyonu göstermiştir. Süre bakımından karşılaştırmak üzere algoritmalar 50 boyutlu test fonksiyonlarında 1000 iterasyonda çalıştırılmış ve elde edilen ortalama çalışma süreleri incelendiğinde, Yapay Deniz Anası Optimizasyonu ve Tunik Sürüsü Optimizasyonu’nun en hızlı çalışan algoritmalar olduğu görülmektedir. Etçil Bitki Optimizasyonu ve Öğrenci Psikolojisine Dayalı Optimizasyon ise en yavaş çalışan algoritmalar olmuştur.

Keywords

• Global Optimizasyon
• Kalite Testi Fonksiyonları
• Metasezgisel Algoritmalar

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