Affiliations: [a] Electrical Power and Machines Department, Faculty of
Engineering, Cairo University, Giza, Egypt | [b] Engineering Mathematics Department, Faculty of
Engineering, Cairo University, Giza, Egypt
Correspondence:
[*]
Corresponding author: A.A. Adly, Electrical Power and Machines
Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt.
E-mail: [email protected]
Abstract: Recently, utilization of electromagnetic suspension systems has been
on the rise as a result of novel space and automotive-related industrial
applications. This paper presents an efficient iterative hybrid neural-swarm
optimization methodology for electromagnetic suspension system design involving
nonlinear magnetic media. Within this approach, 2D field computations are
carried out using the integral equation approach in an automated mechanism
through continuous Hopfield neural network (HNN) implementation. Optimal
dimensions of the system are identified through the minimization of an error
function of some target performance using the particle swarm optimization (PSO)
evolutionary approach. Details of the approach and sample design examples are
given in the paper.
