Turnuva seçim operatörü kullanan bir havai fişek algoritması

Year 2017, Volume: 23 Issue: 5, 628 - 636, 20.10.2017

Bilal Babayiğit , Sema Haspaylan

Abstract

Son on
yılda doğa olaylarından esinlenerek çeşitli sürü zekasına dayalı optimizasyon
teknikleri geliştirilmiştir. Kabul edilebilir bir sürede optimuma yakın
çözümler üretebilen bu teknikler, fen ve sosyal bilimlerdeki birçok problemin
çözümünde başarıyla uygulanmıştır.  Havai
Fişek Algoritması (HFA), havai fişeklerin patlamalarından esinlenilmiş yeni bir
sürü zekası algoritmasıdır. Oldukça yeni sayılabilecek bu teknik, çok çeşitli
problemlerde başarılı bir şekilde kullanılmış ve özellikle parçacık sürü optimizasyonu,
karınca koloni ve genetik algoritma gibi tekniklere göre daha iyi sonuçlar elde
edilmiştir. Elde edilen başarılı sonuçlara rağmen, HFA optimum çözüme ulaşmak
için uzun zamana ihtiyaç duymaktadır. Bu hesaplama zamanı yetersizliğini
giderebilmek amacıyla bu çalışmada turnuva seçimi kullanan bir HFA
önerilmiştir. Turnuva seçme operatörüne sahip HFA’nın başarımı 15 adet nümerik
optimizasyon probleminde test edilmiştir. Deneysel sonuçlar önerilen HFA’nın
klasik HFA’ya göre hesaplama zamanı ve çözüm kalitesinde önemli performans
iyileşmeleri sağladığını göstermiştir.

Keywords

Sürü zekası , Optimizasyon teknikleri , Nümerik optimizasyon

A fireworks algorithm using tournament selection operator

Abstract

In
recent decade, several nature-inspired swarm intelligence-based optimization
techniques have been improved. These techniques, which give solutions close to
optimum in an acceptable time, have been applied successfully to solve the
problems in science and social sciences. Fireworks Algorithm (FA), inspired by
observing fireworks explosion, is a new swarm intelligence algorithm. This
relatively new technique has been utilized to tackle diverse problems and
obtained better performance than other popular techniques such as particle
swarm optimization, ant colony, and genetic algorithm. Despite the good results
obtained, FA requires long computation time to achieve the optimum solution. To
eliminate long computation time drawback of FA, in this study, a FA using
tournament selection is proposed. The performance of the proposed FA, which
involves tournament selection operator, is tested on well-known 15 numerical
optimization problems. Experimental results reveal that proposed FA has a
significant performance improvement in term of computation time and solution
quality in comparison with original FA.

Keywords

Swarm intelligence , Optimization techniques , Numerical optimization

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