Comparison of Current Metaheuristic Algorithms with Different Performance Criteria

Yıl 2023, Cilt: 11 Sayı: 4, 1861 - 1884, 24.10.2023

Sibel Arslan

https://doi.org/10.29130/dubited.1150453

Cited By: 2

Öz

Nowadays, metaheuristics play a very important role in solving optimization problems. In this study, Particle Swarm Optimization Algorithm (PSO), one of the most commonly used metaheuristics, was compared in three new metaheuristic (African Vulture Optimization Algorithm-AVOA, Improved Gray Wolf Optimization Algorithm- I-GWO and Marine Predators Algorithm-MPA) comparisons inspired by swarm intelligence and foraging behavior of creatures in nature. According to the experimental studies, AVOA and MPA achieve more successful results than other algorithms. The statistical significance of the results was evaluated using the Friedman Wilcoxon signed-rank test, and the significant superiority of these two algorithms was proven.

Anahtar Kelimeler

African Vulture Optimization Algorithm, Improved Gray Wolf Optimization Algorithm, Marine Predators Algorithm, Particle Swarm Optimization

Proje Numarası

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Güncel Metasezgisel Algoritmalarının Performansları Üzerine Karşılaştırılmalı Bir Çalışma

Öz

Günümüzde, metasezgiseller optimizasyon problemlerinin çözümünde çok önemli bir rol oynamaktadır. Bu çalışmada sürü zekasından ve doğadaki canlıların yiyecek arama davranışlarından esinlenerek geliştirilen üç yeni metasezgisel (Afrika Akbabaları Optimizasyon Algoritması (African Vulture Optimization Algorithm, AVOA), Geliştirilmiş Gri Kurt Optimizasyon Algoritması (Improved Gray Wolf Optimization Algorithm, I-GWO) ve Deniz Avcıları Algoritması (Marine Predators Algorithm, MPA)), kıyaslamalarda en çok kullanılan metasezgisellerden biri olan Parçacık Sürü Optimizasyon Algoritması (Particle Swarm Optimization, PSO) ile kıyaslanmıştır. Deneysel çalışmalara göre, AVOA ve MPA’nın diğer algoritmalara göre daha başarılı sonuçlara sahip olduğu görülmektedir. Sonuçların istatiksel anlamlılığı, Friedman ve Wilcoxon işaretli sıralar testleri ile değerlendirilerek bu iki algoritmanın üstünlüğü kanıtlanmıştır.

Anahtar Kelimeler

Afrika Akbabaları Optimizasyon Algoritması, Geliştirilmiş Gri Kurt Optimizasyon Algoritması, Deniz Avcıları Algoritması, Parçacık Sürü Optimizasyon Algoritması

Destekleyen Kurum

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Proje Numarası

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Teşekkür

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Kaynakça

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