0-1 problem uzayları için kuantum tabanlı yerel arama ile güçlendirilmiş parçacık sürüsü optimizasyonu

Yıl 2018, Cilt: 24 Sayı: 4, 675 - 681, 17.08.2018

Fehmi Burçin Özsoydan

Öz

Parçacık
Sürü Optimizasyonu (PSO), problem çözmede balık veya kuş sürülerinin
hareketlerini taklit eden, oldukça bilinen sürü zekâsı tabanlı bir
algoritmadır. İlk olarak kısıtsız global optimizasyon problemlerini çözmek için
önerilse de, çeşitli problem tiplerini içeren çok sayıda PSO çalışması
mevcuttur. Fakat bununla birlikte, ilgili bilimsel yazından görülebileceği
gibi, diğer uygulama türleriyle karşılaştırıldığında, kesikli ve 0-1 yapıdaki
PSO uygulamaları görece daha az sayıdadır. Bu bağlamda, mevcut araştırmada,
kuantum tabanlı yerel arama yordamı ile güçlendirilmiş bir 0-1 PSO
modifikasyonu getirilmiştir. Bahsedilen kuantum tabanlı prosedür, algoritma
tarafından bulunan eniyi çözüm etrafında üretilen bir küre içinde konumlanan ve
kuantum parçacıkları olarak adlandırılan rastgele dağıtılmış parçacıklar
üretir. Ardından bu parçacıklar, bulunan eniyi çözüm üzerinde olası
iyileştirmeler sağlayabilmek için yerel arama amacıyla kullanılır. Önerilen
yaklaşımın performansı, bu alanda sıkça kullanılan Bir-Enb, Aldatıcı, Plato ve
Kral Yolu fonksiyonlarından oluşan bir 0-1 problem seti kullanılarak test
edilmiştir. Deneysel çalışma, önerilen yaklaşımın 0-1 problemlerdeki
etkinliğini göstermektedir.

Anahtar Kelimeler

Metasezgiseller, Parçacık Sürüsü Optimizasyonu, 0-1 optimizasyon, Kuantum parçacıkları

A quantum based local search enhanced particle swarm optimization for binary spaces

Öz

Particle
Swarm Optimization (PSO) is a well-known swarm intelligence-based algorithm
that simulates the movements of school or bird flocks in problem solving.
Although it is first introduced to solve unconstrained global optimization
problems, there are numerous reported publications of PSO involving various
types of problems. However, as one can see from the related literature,
compared to other types of implementations, discrete and binary PSO
applications are relatively fewer in number. In this context, in the present
work, a 0-1 PSO modification enhanced with a quantum-based local search
procedure is developed. The mentioned quantum-based procedure generates
randomly scattered particles referred to as quantum particles located within a
sphere that is generated around the best-found solution by the algorithm. Next,
these particles are used for local search to achieve possible improvements on
the best-found solution. The performance of the proposed approach is tested by
using a 0-1 problem suite consisting of the commonly used One-Max, Deceptive,
Plateau and Royal Road functions. Experimental study shows the effectiveness of
the proposed approach in 0-1 problems.

Anahtar Kelimeler

Metaheuristics, Particle swarm optimization, Binary optimization, Quantum particles

Kaynakça

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