Abstract
AbstractAn 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.
Highlights
An inverse design problem is widely known in natural sciences
The present paper introduces the application of an indirect surrogate modeling within velocity update formula to speed up the Parçacık Sürü Optimizasyon (PSO) algorithm and overcome problems such as inaccuracy and premature convergence during the optimization
Optimization Results The swarm particles are optimized in accordance with given objective function by using five PSO algorithms including constriction factor PSO (c-PSO), weight PSO (w-PSO), comprehensive learning PSO (cl-PSO), vibrational PSO (v-PSO), and s-PSO
Summary
An inverse design problem is widely known in natural sciences. Most of the formulations of inverse problems may proceed to the setting of an optimization problem. These algorithms are population based, and they include a lot of design candidates waiting for the objective function computations in each generation. The major weakness of population based algorithms lies in their poor computational efficiency, because the evaluation of objective function is sometimes very expensive [1]. The key idea in these methods is to parameterize the space of possible solutions via a simple, computationally inexpensive model, and to use this model to generate inputs in terms of predicted objective function values for the optimization algorithm. In case of the problem which has a high number of design variables, the construction of surrogate model may cause extremely high computational cost, which means computationally inefficient approximation. The test bed selected includes surface fitting of a wing shape and an inverse design of an airfoil in subsonic flow
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Schedule a callMore From: Journal of Aeronautics and Space Technologies (Havacilik ve Uzay Teknolojileri Dergisi)
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