Volume 14, Issue 4 (10-2024)
IJOCE 2024, 14(4): 647-663 |
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Kaveh A, Khavaninzadeh N. NODAL ORDERING OF GRAPHS FOR WAVEFRONT OPTIMIZATION USING NEURAL NETWORK AND WATER STRIDER ALGORITHMS. IJOCE 2024; 14 (4) :647-663
URL: http://ijoce.iust.ac.ir/article-1-613-en.html
URL: http://ijoce.iust.ac.ir/article-1-613-en.html
1- School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran-16, Iran
Abstract: (742 Views)
In this paper, a neural network is trained for optimal nodal ordering of graphs to obtain a small wavefront using soft computing. A preference function consists of six inputs that can be seen as a generalization of Sloan's function. These six inputs represent the different connection characteristics of graph models. This research is done with the aim of comparing Sloan's theoretical numbering method with Sloan's developed method with neural networks and WSA meta-heuristic algorithm. Unlike the Sloan algorithm, which uses two fixed coefficients, six coefficients are used here, based on the evaluation of artificial neural networks. The weight of networks is obtained using Water Strider algorithm. Examples are included to demonstrate the performance of the present hybrid method.
Type of Study: Research |
Subject:
Optimal analysis
Received: 2024/11/14 | Accepted: 2024/12/28 | Published: 2024/10/16
Received: 2024/11/14 | Accepted: 2024/12/28 | Published: 2024/10/16
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