سال انتشار: ۱۳۹۴

محل انتشار: کنفرانس ملی فن آوری، انرژی و داده با رویکرد مهندسی برق و کامپیوتر

تعداد صفحات: ۶

نویسنده(ها):

Nastaran Aaghaee – Electrical Engineering Department Faculty of Engineering, Razi University Kermanshah, Iran
Mohsen Hayati – Electrical Engineering Department Faculty of Engineering, Razi University Kermanshah, Iran
Ehsan Valian – Electrical & Electronic Engineering Department Faculty of Engineering, Shahed University Tehran, Iran

چکیده:

Grey Wolf Optimizer (GWO) is a metaheuristic optimization method inspired by grey wolves which is suitable for solving optimization problems. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. First, grey wolf optimizer is presented. Next, it is employed for training feedforward neural networks for two benchmark classification problems. Then, the performance of GWO is compared with that of back-propagation (BP) methods. Simulation results demonstrate the effectiveness of the GWO algorithm.