کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6855079 1437604 2018 35 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Neural network and fuzzy system for the tuning of Gravitational Search Algorithm parameters
ترجمه فارسی عنوان
شبکه عصبی و سیستم فازی برای تنظیم پارامترهای الگوریتم جستجوی گرانشی
کلمات کلیدی
هوش محاسباتی، سیستم های فازی الگوریتم جستجوی گرانشی، شبکه های عصبی، الگوریتم های جستجو
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
A good trade-off between exploration and exploitation to find optimal values in search algorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increase problems. The Gravitational Search Algorithm (GSA) is a swarm optimization algorithm based on the law of gravity, where the solution search process depends on the velocity of particles. The application of intelligent techniques can improve the search performances of GSA. This paper proposes the design of a Neuro and Fuzzy Gravitational Search Algorithm (NFGSA) to achieve better results than GSA in terms of global optimum search capability and convergence speed, without increasing the computational complexity. Both the algorithms have the same computational complexity O(nd), where n is the number of agents and d is the search space dimension. The main task of the designed intelligent system is to adjust a GSA parameter on a revised version of GSA. NFGSA is compared with GSA, a Plane Surface Gravitational Search Algorithm (PSGSA) and a Modified Gravitational Search Algorithm (MGSA). The results show that NFGSA improves the optimization performances of GSA and PSGSA, without adding computational costs. Moreover, the proposed algorithm is better than MGSA for a benchmark function and achieves similar results for two test functions. The analysis on the computational complexity shows that NFGSA has a better computational complexity than MGSA, because NFGSA has complexity O(nd), whereas MGSA has complexity O((nd)2).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 102, 15 July 2018, Pages 234-244
نویسندگان
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