Parçacık Sürü Optimizasyon Algoritmasında Bir Topluluk Atalet Ağırlığı Hesaplama Stratejisi

Parçacık sürüsü optimizasyonunun nihai başarısı, önceki parçacıkların hız değerlerine bağlıdır. Hız,atalet ağırlık katsayısı ile çarpılır ve parçacık sürüsü optimizasyonunun arama yeteneği üzerinde önemlibir etkiye sahiptir. Bu katsayıyı hesaplamak için yapılan önceki çalışmalara bakıldığında atalet ağırlıkkatsayısının çeşitli şekillerde ele alındığı görülmektedir. Bu makalede; diğer sabit, rasgele, doğrusalazalan, küresel yerel en iyi, benzetimli tavlama ve kaotik atalet ağırlığı hesaplama yöntemlerinikullanılan yeni bir topluluk atalet ağırlığı hesaplama stratejisi önerilmiştir. Önerilen yöntemde, diğeryöntemlerin sonuçları uygun bir şekilde birleştirilerek nihai çıktı kararı üretmek için kullanılmaktadır.Deneysel testlerde, bilinen 30 optimizasyon kıyaslama test problemi kullanılmaktadır. Önerilen toplulukstratejisi istatistiksel testlerle kanıtlanmış ve tüm optimizasyon kıyaslama test problemlerinde başarılısonuçlar vermiştir.

Anahtar Kelimeler:

Atalet ağırlığı, Parçacık sürü optimizasyonu

AN ENSEMBLE INERTIA WEIGHT CALCULATION STRATEGY IN PARTICLE SWARM OPTIMIZATION ALGORITHM

The ultimate success of particle swarm optimization depends on the velocity values ofprevious particles. Velocity is multiplied with inertia weight coefficient, and has a significant effect onsearch capability of the particle swarm optimization. When looking at previous studies that are carriedout to calculate this coefficient, it is seen that inertia weight coefficient has been handled in several ways.In this article; a new ensemble inertia weight calculation strategy is proposed that uses other constant,random, linear decreasing, global local best, simulated annealing and chaotic inertia weight calculationmethods. Other methods results are combined and used to make a final output decision in a proper way.In experimental tests, 30 common optimization benchmark test problems are used. Proposed ensemblestrategy is proven by statistical tests and gives successful results in all optimization benchmark testproblems.

Keywords:

Inertia weight, Particle swarm optimization,

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