Diferansiyel Evrim Algoritması Temelli Global Arama Stratejisi ile Geliştirilmiş Yeni Bir Yapay Denizanası Arama Algoritması

Year 2022, Volume: 9 Issue: 4, 1178 - 1192, 31.12.2022

Gülnur Yıldızdan

https://doi.org/10.31202/ecjse.1131734

Abstract

Metasezgisel algoritmalar, bir problemi çözmek için olası çözümlerden daha etkili olanına karar vermek için kullanılan ve doğal fenomenlerden esinlenen algoritmalardır. Her geçen gün sayıları artmakta olan bu algoritmalar, kesin çözümü garanti etmemesine rağmen kesin çözüm etrafındaki bir çözüme hızlı şekilde ulaşmayı vadeder. Yapay Denizanası Arama Algoritması(YDA) da 2021 yılında önerilmiş yeni bir metasezgisel algoritmadır. Bu çalışmada, YDA’nın global arama kabiliyetini geliştirmek amacıyla standart algoritmanın global arama bölümünde bir düzenleme yapılmıştır. Buna göre, Diferansiyel Evrim Algoritmasındaki başarılı mutasyon stratejilerinden biri olan “current-to-best” yaklaşımı, YDA’nın global arama yöntemine entegre edilmiştir. Bu düzenleme sonucu elde edilen gelişmiş algoritma(MYDA),yedi tanesi tek modlu, beş tanesi çok modlu özellikte olmak üzere toplam on iki kıyaslama fonksiyonu üzerinde 10,30,50,100,500 ve 1000 boyut için test edilmiştir. Ayrıca MYDA, literatürden seçilen algoritmalarla da karşılaştırılmıştır. Sonuçlar istatistik testler yardımıyla yorumlanmıştır. Elde edilen sonuçlar incelendiğinde, önerilen algoritmanın tüm fonksiyonlarda tüm boyutlar için standart algoritmadan daha iyi performans gösterdiği tespit edilmiştir. Literatürle yapılan karşılaştırmada ise algoritmanın başarılı ve yarışmacı sonuçlar ürettiği belirlenmiştir.

Keywords

Diferansiyel evrim algoritması, metasezgisel algoritma, sürekli optimizasyon, yapay denizanası arama algoritması, klasik kıyaslama fonksiyonları

A Novel Artificial Jellyfish Search Algorithm Improved with a Differential Evolution Algorithm-Based Global Search Strategy

Abstract

Metaheuristic algorithms are algorithms inspired by natural phenomena and that are used to decide which possible solution is more efficient to solve a problem. Although these algorithms, whose numbers are increasing day by day, do not guarantee the exact solution, they promise to reach a solution around the exact solution quickly. Artificial Jellyfish Search Algorithm (YDA) is also a new metaheuristic algorithm proposed in 2021. In this study, a modification has been made to the global search part of the standard algorithm in order to improve the global search capability of YDA. Accordingly, the "current-to-best" approach, which is one of the successful mutation strategies in the Differential Evolution Algorithm, has been integrated into the global search method of YDA. The advanced algorithm (MYDA) obtained as a result of this modification has been tested for 10,30,50,100,500 and 1000 dimensions on a total of twelve benchmark functions, seven of which are uni-modal and five are multi-modal. In addition, MYDA has also been compared with algorithms selected from the literature. The results have been interpreted with the help of statistical tests. When the results obtained are examined, it has been determined that the proposed algorithm outperforms the standard algorithm for all dimensions in all functions. In the comparison with the literature, it has been determined that the algorithm produces successful and competitive results.

Keywords

Differential evolution algorithm, metaheuristic algorithm, continuous optimization, artificial jellyfish search algorithm, classic benchmark functions

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