Son Üç Yılda Geliştirilen Metasezgisel Algoritmalar Hakkında Kısa Bir İnceleme

Abstract

Optimizasyon bir problemin olabilecek farklı çözümleri arasından en iyi sonucu verenin bulunmasıdır. Optimizasyon problemlerinin çözümünde birçok algoritma kullanılmaktadır. Optimizasyon algoritmaları genel olarak sezgisel optimizasyon algoritmaları ve matematiksel optimizasyon algoritmaları olarak ikiye ayrılmaktadır. Matematiksel optimizasyon algoritmaları, tüm çözüm kümesini tarayarak çözüme ulaşmayı amaçlarken, sezgisel optimizasyon algoritmaları ise, çözüm kümesine sezgisel olarak yaklaşmakta ve en iyi çözüme yada en iyiye yakın bir çözüme ulaşmayı amaçlamaktadır. Çözüm kümesi geniş olan problemlerde matematiksel optimizasyon algoritmalarının kullanımı maliyetlidir. Bu tip problemlerin çözümünde, sezgisel optimizasyon algoritmaları daha avantajlı olup daha fazla tercih edilmektedir. Bir optimizasyon algoritmasının her tür problem veya test fonksiyonu üzerinde başarılı olması beklenemez. Bu nedenle hangi tür problemin hangi algoritma ile daha iyi çözüldüğünün belirlenmesi gerekmektedir. Günümüzde temel sezgisel metotların birleşerek etkili kullanımı sonucunda Metasezgisel isimli algoritmalar geliştirilmiştir. Bu algoritmalar, yüksek seviyeli çalışma ortamında, verimli arama işlemleri kullanarak çözüm uzayındaki optimum çözüme daha hızlı şekilde ulaşmaktadır. Metasezgisel optimizasyon tekniklerinin kullanımının yaygın olması nedeniyle, günümüzde birçok yeni metasezgisel optimizasyon algoritmaları önerilmektedir. Önerilen bu algoritmalar üzerinde geliştirmeler ve hibrit çalışmalar da yapılmaktadır. Bu çalışmada, literatürde son üç yılda (2017-2019) önerilmiş olan, Harris Hawks Optimizasyon Algoritması (HHO), Satin Bowerbird Optimizasyon Algoritması (SBO), Optimal Foraging Algoritması (OFA), Butterfly Optimizasyon Algoritması (BOA), Pity Beetle Algoritması (PBA) ve Collective Decision Optimizasyon Algoritması (CDOA) ele alınmıştır. Geliştirilen bu yeni optimizasyon algoritmalarının esinlendikleri alan, çalışma mantıkları ve arama stratejileri incelenerek sunulmuştur. Gerçekleştirilen bu derlemenin metasezgizel optimizasyon problemleri alanında yapılacak olan çalışmalara ışık tutacağı düşünülmektedir.

Keywords

• Optimizasyon
• Metasezgisel
• Arama algoritmaları

A Brief Review of Metaheuristic Algorithms Improved in the Last Three Years

Abstract

Optimization is to find the best solution among the different solutions of a problem. Many algorithms are used to solve optimization problems. Optimization algorithms are generally divided into heuristic optimization algorithms and mathematical optimization algorithms. While mathematical optimization algorithms aim to reach the solution by scanning the whole solution set, heuristic optimization algorithms approach the solution set intuitively and aim to reach the best solution or the best solution. Mathematical optimization algorithms are costly to solve problems with a large set of solutions. In solving such problems, heuristic optimization algorithms are more advantageous and more preferred. An optimization algorithm cannot be expected to succeed on any problem or test function. Therefore, it is necessary to determine which kind of problem is best solved by which algorithm. Nowadays, as a result of the effective use of basic heuristic methods combined, Metaheuristic algorithms have been developed. These algorithms reach the optimum solution in the solution space faster by using efficient search operations in a high level working environment. Due to the widespread use of metaheuristic optimization techniques, many new metaheuristic optimization algorithms have been proposed today. Improved and hybrid studies are also performed on these proposed algorithms. In this study, Harris Hawks Optimization Algorithm (HHO), Satin Bowerbird Optimization Algorithm (SBO), Optimal Foraging Algorithm (OFA), Butterfly Optimization Algorithm (BOA) and Pity Beetle Algorithm (PBA) and Collective Decision Optimization Algorithm (CDOA) have been proposed in the literature in the last three years (2017-2019). The field of inspiration of these new optimization algorithms, study logic and search strategies are presented. It is thought that this review will shed light on the studies to be conducted in the field of metaheuristic optimization problems.

Keywords

• Optimization
• Metaheuristic
• Search algorithm

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