A modified lambda algorithm for optimization in electromagnetics
ISSN: 0332-1649
Article publication date: 29 April 2014
Abstract
Purpose
The purpose of this paper is to show with the help widely used analytical and application-oriented benchmark problems that a novel and relatively uncommon optimization method, lambda optimization, can be successfully applied to the solution of optimization problems in electromagnetics. Furthermore an improvement to the method is proposed and its effectiveness is validated.
Design/methodology/approach
An adaptive probability factor is used within the framework of lambda optimization.
Findings
It is shown that in the framework of lambda optimization (LO) the use of an adaptive probability factor can provide high-quality solutions with small standard deviation on the selected benchmark problem.
Research limitations/implications
Although the chosen benchmarks are considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.
Practical implications
The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.
Originality/value
This paper introduces and validates the use of adaptive probability factor in order to improve the balance between the explorative and exploitative characteristics of the LO algorithm.
Keywords
Acknowledgements
This work was supported by the National Council of Scientific and Technologic Development of Brazil – CNPq – under Grant Nos 476235/2011-1/PQ and 304785/2011-0/PQ and University of Padova PRAT2011 Grant No. CPDA115285.
Citation
Alotto, P., dos Santos Coelho, L., C. Mariani, V. and da C. Oliveira, C. (2014), "A modified lambda algorithm for optimization in electromagnetics", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 33 No. 3, pp. 759-767. https://doi.org/10.1108/COMPEL-10-2012-0224
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited