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Sürü zekâsında yeni bir yaklaşım: Kuş sürüsü algoritması

A new approach to swarm intelligence: Bird Swarm Algorithm

Journal Name:

  • Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi

Publication Year:

  • 2017

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Optimization known as also mathematical programming, is a collection of processes that selects the most appropriate values of decision variables according to a goal (evaluation) function. Many algorithms have been proposed for optimization problems. Most of these algorithms need mathematical models for model of system and objective function. Metaheuristics algorithms are the algorithms that utilize a simple approach as a solution technique of search and optimization problems and are recently getting strong and becoming more popular due to their advantages. They are population based techniques and begin to search the solution with multiple points. They have good reputation and wide acceptability as being powerful tools for many different fields such as management science, engineering, computer, etc. and new versions of these algorithms have been proposed. General purpose heuristic optimization algorithms are evaluated in eight different groups including biology-based, physics-based, swarm-based, socialbased, music-based, chemical-based, sports based, and mathematics based. Due to the philosophy of continually searching the best and absence of the most efficient metaheuristic method for all types of problems, novel algorithms or new variants of current algorithms are being proposed. Swarm optimization algorithms are relatively newer subfield of computational intelligence and recently getting strong and becoming more popular. They have been developed by observing the movements of live swarms such as bird, fish, cat and bee. They are inspired from intelligent behaviors resulting from the local interactions of swarm agents between each other and environment. They are adaptable and general purposed solution methods that can be applied to the high-scale combinatorial and nonlinear search and optimization problems in case of concurrent different decision variables, objective functions, and constraints and they do not depend on the solution space type, the number of decision variables, and the number of constraint functions. Furthermore, they do not require very well defined mathematical models that are hard to derive. Their computation power is also good and they do not require excessive computation time. Their transformations and adaptations are easy. Due to these advantages, these algorithms are densely being used in many different fields. In this work, bird swarm algorithm that is one of the most current swarm intelligence optimization algorithm was studied in detail. In this study, the performance of this new algorithm is tested within unimodal and multi modal benchmark functions with different dimensions. In these investigations, tendency of converging to optimum and the obtained result values are used as a measure of performance. Experimental results have been presented and interpreted through comparative tables. It is expected that this algorithm will be efficiently used in many different types of complex problems due to high performance of the algorithm in both unimodal and multi modal functions.
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Abstract (Original Language): 
Matematiksel programlama olarak da bilinen optimizasyon, bir amaç (değerlendirme) fonksiyonuna göre bir problemde belirli aralıktaki sayısal değerlerin en uygununu seçen işlemler topluluğudur. Optimizasyon problemleri için birçok algoritma önerilmiştir. Bu algoritmaların çoğu sistemin modeli ve amaç fonksiyonu için matematiksel modellere ihtiyaç duymaktadır. Sürü zekâsına dayalı algoritmalar, büyük boyutlu optimizasyon problemleri için, kabul edilebilir sürede optimum ya da optimuma yakın çözümler verebilen algoritmalardır. Matematiksel modelin çıkarılamadığı durumlarda kabul edilebilir sürede sonuç elde edebilmek amacıyla genel amaçlı sezgisel optimizasyon algoritmaları kullanılır. Genel amaçlı sezgisel optimizasyon algoritmaları, biyoloji tabanlı, fizik tabanlı, sürü tabanlı, sosyal tabanlı, müzik tabanlı, kimya tabanlı, spor tabanlı ve matematik tabanlı olmak üzere sekiz farklı grupta değerlendirilmektedir. Sürü zekâsı tabanlı optimizasyon algoritmaları kuş, balık, kedi ve arı gibi canlı sürülerinin hareketlerinin incelenmesiyle geliştirilmiştir. Bu çalışmada, sürü zekâsı optimizasyon algoritmalarının en güncellerinden biri olan kuş sürüsü optimizasyon algoritması ayrıntılı olarak incelenmiştir. Bu algoritmanın performansı, farklı boyutlardaki tek modlu ve çok modlu kalite testi fonksiyonları kullanılarak test edilmiştir. Yapılan deneylerde, optimuma yakınsama eğilimi ve elde edilen sonuç değerleri, performans ölçütü olarak kullanılmıştır. İnceleme sonuçları karşılaştırmalı tablolar aracılığıyla sunulmuş ve yorumlanmıştır. Bu algoritma ile hem tek modlu hem de çok modlu kalite testi fonksiyonlarında diğer sürü zekâsı algoritmalarından çok daha iyi sonuçlar elde edildiği için, algoritmanın ileride birçok problemde etkili olarak kullanılacağı beklenmektedir.
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