IMPROVED PARTICLE SWARM OPTIMIZATION METHOD DIRECTED BY INDIRECT SURROGATE MODELING

Bu çalışma kapsamında yeni bir Parçacık Sürü Optimizasyon (PSO) algoritması geliştirilmiş ve teklif edilen algoritma iki farklı test probleminde denenmiştir. Söz konusu test problemleri kanat yüzeyi modelleme ve sesaltı akış şartlarında kanat profilinin tersten tasarımıdır. Teklif edilen yeni algoritma dolaylı vekil model kullanımına dayalı olarak öngörülen aday çözümün PSO algoritmalarındaki temel güncelleme denklemlerine ilave edilmesini öngörmektedir. Çalışma dahilinde dikkate alınan problemlerin tümünde teklif edilen algoritmanın kayda değer hesaplama süresi azaltımları sağladığı görülmüştür.

DOLAYLI VEKİL MODEL İLE YÖNLENDİRİLEN GELİŞTİRİLMİŞ PARÇACIK SÜRÜ ENİYİLEME ALGORITMASI

An improved particle swarm optimization algorithm is proposed and tested for two different test cases: surface fitting of a wing shape and an inverse design of an airfoil in subsonic flow. The new algorithm emphasizes the use of an indirect design prediction based on a local surrogate modeling as a part of update equations in particle swarm optimization algorithm structure. For all the demonstration problems considered herein, remarkable reductions in the computational times have been accomplished.

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