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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 24 Sayı: 3

Performance Evaluation of Meta-Heuristic Optimization Algorithms with The TOPSIS Approach

TOPSIS Yaklaşımı ile Metasezgisel Optimizasyon Algoritmalarının Performans Değerlendirmesi

Şehmus Fidan(BATMAN ÜNİVERSİTESİ, BATMAN MESLEK YÜKSEKOKULU)0000-0002-5249-7245
Metin Zaloğlu(BATMAN ÜNİVERSİTESİ)0009-0003-0589-8868
Emre Erkan(BATMAN ÜNİVERSİTESİ, BATMAN MESLEK YÜKSEKOKULU)0000-0003-0187-4079
Pages
726–748
Publication Date
2024-06-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of the study, determining the best algorithm ranking has been accomplished in a straightforward and practical manner.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Meta-heuristic algorithms inspired by nature can be utilized to derive a mathematical model of a system based on input/output data.
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Key Findings & Data:Additionally, their performances have been compared under constraints such as time, maximum function evaluations, early stopping, and maximum generations.
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Conclusion & Impact:As a result of the study, determining the best algorithm ranking has been accomplished in a straightforward and practical manner.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Metasezgisel Algoritma · Sistem Tanımlama · Transfer Fonksiyonu

Abstract

Meta-heuristic algorithms inspired by nature can be utilized to derive a mathematical model of a system based on input/output data. To achieve this, various meta-heuristic optimization algorithms such as artificial ecosystem optimization (AEO), flower pollination algorithm (FPA), ant lion optimizer (ALO), moth-flame optimization (MFO), tug of war optimization (TWO), atomic search optimization (ASO), brain storm optimization (BSO), water cycle algorithm (WCA), coral reefs optimization (CRO), and life choice-based optimization (LCO) have been considered and employed to optimize the parameters of the proposed transfer function. Additionally, their performances have been compared under constraints such as time, maximum function evaluations, early stopping, and maximum generations. However, in this context, alongside performance metrics such as MAE, MAPE, and R2, metrics specific to transfer functions like rise time, settling time, and overshoot also emerge. The multitude of metrics makes it challenging to determine which algorithm performs best. To overcome this difficulty, the use of a multi-criteria decision-making approach known as Topsis (Technique for Order Preference by Similarity) is proposed in this study. The algorithm's solution time, performance (R2), and rise time have been considered for multiple criteria. As a result of the study, determining the best algorithm ranking has been accomplished in a straightforward and practical manner.

Özet

Bir sistemin sadece giriş/çıkış verilerinin kullanılarak matematiksel bir model elde etmek için doğadan ilham alan metasezgisel algoritmalar kullanılabilir. Bunu gerçekleştirmek için yapay ekosistem (YEA), çiçek tozlaşma (ÇTA), güve-alev (GAA), karınca aslanı algoritması (KAA), halat çekme (HÇA), atom arama (AAA), beyin fırtınası (BFA), su döngüsü (SDA), mercan resifleri (MRA) ve yaşam seçimi tabanlı algoritma (YSTA) gibi çeşitli metasezgisel optimizasyon algoritmaları ele alınmış ve önerilen transfer fonksiyonunun parametrelerini optimize etmek için kullanılmıştır. Ayrıca zaman, maksimum fonksiyon, erken durdurma ve maksimum generasyon sınırlılıkları altında performanslar karşılaştırılmıştır. Ancak bu durumda MAE, MAPE, R2 gibi performans metriklerinin yanında transfer fonksiyonlarına özgü yükselme zamanı, oturma zamanı, aşım miktarı gibi metrikler de ortaya çıkmaktadır. Çok sayıdaki metrik hangi algoritmanın en iyi olduğunu belirlemeyi zorlaştırmaktadır. Bu zorluğun üzerinden gelmek için bu çalışmada Topsis (Technique for Order Preference by Similarity) olarak anılan çok kriterli bir karar verme yaklaşımının kullanımını önerilmiştir. Çoklu kriter için algoritmanın çözüm zamanı, performans (R2) ve yükselme zamanı dikkate alınmıştır. Yapılan çalışma neticesinde en iyi algoritma sıralamasını belirlemek oldukça kolay ve pratik bir şekilde gerçekleştirilmiştir.

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Cite This Article

Fidan, Ş., Zaloğlu, M., Erkan, E. (2024). Performance Evaluation of Meta-Heuristic Optimization Algorithms with The TOPSIS Approach. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 24 Sayı: 3, 726–748. https://doi.org/10.35414/akufemubid.1387447

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