Solving engineering optimization problems with the Karush-Kuhn-Tucker hopfield neural networks

Ganesan, T. and Elamvazuthi, I. and Vasant, P. (2011) Solving engineering optimization problems with the Karush-Kuhn-Tucker hopfield neural networks. International Review of Mechanical Engineering, 5 (7). pp. 1333-1339. ISSN 19708734

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

The Karush-Kuhn-Tucker (KKT) approach is a well established classical method to solve non-linear programming (NLP) optimization problems. The aim of this work is to integrate the KKT method into the Hopfield Neural Networks (HNN) and hence create a new algorithm, the KKT-hopfield neural networks (KHN) for solving nonlinear optimization problems that contain inequality constraints. In this work, the development and the testing of the KHN algorithm was carried out. The KHN algorithm was used for solving two engineering design problems which were; 'optimization of the design of a pressure vessel' (P1) and the 'optimization of the design of a tension/compression spring' (P2). The computational performance of the KHN algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on similar engineering problems. Comparative studies and analysis were then carried out based on the optimized results. © 2011 Praise Worthy Prize S.r.l. - All rights reserved.

Item Type: Article
Additional Information: cited By 29
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:50
Last Modified: 09 Nov 2023 15:50
URI: https://khub.utp.edu.my/scholars/id/eprint/1845

Actions (login required)

View Item
View Item