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

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

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.

Keywords:

Metaheuristics, Particle swarm optimization, Binary optimization, Quantum particles,

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