Gray Wolf and Krill Herd optimizations: Performance analysis and comparison

Year 2023, Volume: 29 Issue: 7, 711 - 736, 30.12.2023

Emine Bas Aysegul Ihsan

Abstract

Herding behavior is defined as a group of animals of similar size that migrate in the same direction and hunt together. Gray wolves are usually seen in packs. Each gray wolf in the herd has a distinct duty and a distinct name that reflects the task. Krill swarms form the basis of ocean ecology. There are two reasons for the movement of the Krill herd. The first reason is that difficult for other organisms to prey on Krill living in herds. Another compelling factor is the way Krill form vast herds and effortlessly seize their prey. Gray Wolf Optimization (GWO) is inspired by gray wolf herding behavior, while Krill Herd Optimization (KHO) is based on krill herding. In this study, GWO and KHO algorithms are examined in detail and it is decided whether they had sufficient success. The fact that the GWO and KHO algorithms are swarm-based is accepted as a common feature of the two algorithms. However, compared with GWO and KHO analysis, as well as 23 single-mode, multimodal, and fixed-size multimodal benchmarking optimization tests. In another hand, the success of the algorithms has been demonstrated by running them on various dimensions ({10, 20, 30, 50, 100, 500}). Additionally, the performances of the GWO and KHO are compared with Tree Seed Algorithm (TSA), Particle Swarm Algorithm (PSO), Jaya algorithm, Arithmetic Optimization Algorithm (AOA), Evolutionary Mating Algorithm (EMA), Fire Hawk Optimizer (FHO), Honey Badger Algorithm (HBA) algorithms. Moreover, all of the analyses are obtained in detail, complete with statistical tests and figures. As a result, while GWO and KHO algorithms show superior success in different test problems with their own characteristics, they are at a competitive level with many old and newly proposed algorithms today. In order to determine the success of the GWO and KHO algorithms, not only the classical test functions but also two different benchmark test sets are used. These are the CEC-C06 2019 functions and the big data problem, which is a current problem today. The same algorithms are run for both problems and rank values are obtained according to the average results. In CEC-C06 2019 functions, KHO achieved good results, while in big data problems, GWO achieved good results. In this study, the success of the GWO and KHO algorithms are examined in detail in three different experimental sets and it sheds light on researchers who will study with GWO and KHO algorithms.

Keywords

Gray wolf, Krill herd, Optimization algorithm

Gri Kurt ve Kril Sürü optimizasyonları: Performans analizi ve karşılaştırması

Abstract

Sürü davranışı, aynı yönde göç eden ve birlikte avlanan benzer büyüklükteki bir grup hayvan olarak tanımlanmaktadır. Gri kurtlar, genellikle sürüler halinde yaşamaktadırlar. Sürüdeki her gri kurdun ayrı bir görevi ve görevine göre aldığı farklı bir ismi bulunmaktadır. Diğer yandan Kril sürüleri, ekosistemin temelini oluşmaktadır. Kril sürüsünün hareketi iki sebebi bulunmaktadır. Birinci sebep, diğer canlılar için sürüler halinde yaşayan Kril’in avlanması ve yakalanmasının zor olmasıdır. Diğer sebebi ise, Kril sürüleri avlarını sürü hareketiyle kolayca yakalayabilmektedir. Gri Kurt Optimizasyonu (GWO) gri kurt sürü davranışından ilham alınırken, Kril Sürü Optimizasyonu (KHO) Kril sürü davranışından esinlenmiştir. Bu çalışmada GWO ve KHO algoritmaları detaylı bir şekilde incelenmiş ve yeterli bir başarıya sahip olup olmadıklarına karar verilmiştir. GWO ve KHO algoritmalarının sürü tabanlı olması, iki algoritmanın ortak bir özeliği olarak kabul edilmektedir. Ayrıca, GWO ve KHO performans analizinin yanı sıra 23 tek modlu, çok modlu ve sabit boyutlu çok modlu kıyaslama optimizasyon testleri ile karşılaştırılmıştır. Algoritmaların başarısı, çeşitli boyutlarda ({10, 20, 30, 50, 100, 500}) çalıştırılarak gösterilmiştir. İlaveten, GWO ve KHO algoritmaları Ağaç Tohum Algoritması (TSA), Parçacık Sürü Algoritması (PSO), Jaya algoritması, Aritmetik Optimizasyon Algoritması (AOA), Evrimsel Çiftleşme Algoritması (EMA), Ateş Şahini Optimize edicisi (FHO), Bal Porsuğu Algoritması (HBA) algoritmalarının performansı ile de karşılaştırılmıştır. Elde edilen tüm sonuçlar, istatistiksel testler ve şekillerle detaylı olarak gösterilmektedir. Sonuç olarak GWO ve KHO algoritmaları kendine öz özellikleri ile farklı test problemlerinde üstün başarı gösterirken, eski ve günümüzde yeni önerilmiş birçok algoritma ile de yarışır düzeydedir. GWO ve KHO algoritmalarının başarılarını tespit etmek için sadece klasik test fonksiyonları değil iki farklı kıyaslama test seti de kullanılmıştır. Bunlar CEC-C06 2019 fonksiyonları ve günümüzde güncel bir problem olan büyük veri problemidir. Aynı algoritmalar her iki problem içinde çalıştırılmış ve ortalama sonuçlara göre rank değerleri elde edilmiştir. CEC-C06 2019 fonksiyonlarında KHO iyi sonuçlar elde ederken büyük veri problemlerinde GWO iyi sonuçlar elde etmiştir. Bu çalışmada GWO ve KHO algoritmalarının başarıları üç farklı deneysel sette detaylı bir şekilde incelenmiş ve GWO ve KHO algoritmaları ile çalışacak araştırmacılar için ışık tutmaktadır.

Keywords

Gri kurt, Krill sürüsü, Optimizasyon algoritması

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