Parçacık Sürü Optimizasyonu ile Depremin Dış Merkezinin belirlenmesi: Ayvacık Depremi Örneği

Abstract

Optimizasyon problemlerinin çözümü için kullanılan birçok optimizasyon tekniği doğadaki olaylardan esinlenilerek geliştirilmiştir. Parçacık Sürüsü Optimizasyonu (PSO), yiyecek veya ortak hedef arayışında sürü (kuş sürüleri, balık sürüleri, böcekler vb.) davranışını işbirlikçi bir şekilde benimseyen, doğadan ilham alan optimizasyon algoritmalarından biridir. Sürüdeki parçacıklar (ya da ajanlar), arama uzayında kendilerini geliştirmelerinin yanı sıra komşularından da bilgi öğrenirler. Bir parçacığın arama algoritması, süreç sırasında o parçacığın en iyi konumu (bireysel öğrenme terimi) ve belirli bir yinelemede çevresindeki en iyi parçacık (sosyal öğrenme terimi) tarafından belirlenir. PSO'daki temel arama stratejisi, sürüdeki parçacıkların bilişsel bilgilerinin ve sosyal davranışlarının sürekli güncellenmesi yoluyla algoritmayı en iyi çözüme doğru yönlendirmesidir. Bu çalışmada önce yöntemin performansını sentetik model ile test edildikten sonra Çanakkale-Ayvacık depreminin dış merkezinin belirlenmesinde bu algoritmanın uygulaması gösterilmiştir. Bu çalışma sonucunda, Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) tarafından yayınlanan 06.02.2017 depreminin dış merkezi (26.1351, 39.5303) ile PSO çözümü (26.03,39.50) bulunmuştur. Boylam ve enlem için yüzde bağıl hatalar sırasıyla % 0.402 ve %0.077 bulunmuş ve ortalama yüzde bağıl hata %0.239 olarak hesaplanmıştır.

Keywords

• Parçacık Sürü Optimizasyonu
• Sürü Zekâsı
• Deprem dış merkezi
• Çanakkale-Ayvacık
• Metasezgisel

Determining the Epicenter of an Earthquake with Particle Swarm Optimization: Ayvacik Earthquake Example

Abstract

Many optimization techniques used for the solution of optimization problems have been developed by being inspired by the events in nature. Particle Swarm Optimization (PSO) is one of the nature-inspired optimization algorithms that cooperatively adopts swarm behavior (e.g. flocks of birds, flocks of fish, insects, etc.) in search of food or common target. Particles (or agents) in the swarm learn information from their neighbors as well as evolve themselves in search space. A particle's search algorithm is determined by that particle's best position during the process (individual learning term) and the best particle around it at a given iteration (social learning term). The basic search strategy in PSO is to guide the algorithm towards the best solution through continuous updating of the cognitive information and social behavior of the particles in the swarm. In this study, the performance of the method was tested with a synthetic model, and then the application of these algorithms in determining the epicenter of the Canakkale-Ayvacik earthquake was demonstrated. As a result of this study, the epicenter of the 06.02.2017 earthquake published by the Disaster and Emergency Management Presidency (AFAD) and the PSO solution were found as (26.1351, 39.5303) and (26.03, 39.50), respectively. The percent relative errors for longitude and latitude are determined as 0.402%, and 0.077%, and the mean percent relative error is computed as 0.239%

Keywords

• Particle swarm optimization
• Swarm Intelligence
• Epicenter
• Canakkale-Ayvacik
• Metaheuristic

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