NDLI: Genetic algorithm (GA) and particle swarm optimization (PSO) in engineering electromagnetics

Content Provider	IEEE Xplore Digital Library
Author	Rahmat-Samii, Y.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA (Rahmat-Samii, Y.)
Abstract	Modern antenna designers are constantly challenged to seek for optimum solutions for complex electromagnetic device designs. The temptation has grown because of ever increasing advances in computational power. The standard brute force design techniques are systematically being replaced by the state-of-the-art optimization techniques. The ability of using numerical methods to accurately and efficiently characterizing the relative quality of a particular design has excited the EM engineers to apply stochastic global optimizers. The genetic algorithm (GA) is the most popular of the so-called evolutionary methods in the electromagnetics community. Recently, a new stochastic algorithm called particle swarm optimization (PSO) has been shown to be a valuable addition to the electromagnetic design engineer's toolbox. In this paper we provide an overview of both techniques and present some representative examples. Most of the material incorporated in this invited plenary session paper is based on the earlier publication work by the author and his students at UCLA.
Sponsorship	Ministry of Sci. and Technol. of Republic of Croatia Croatian Telecom Inc., Zagreb VIPnet d.o.o., Zagreb City of Zagreb Istituto Italiano di Cultura Zagabria Storm Comput. d.o.o., Zagreb Transmitters and Relays (OIV), Zagreb Micro-Link d.o.o., Zagreb
Starting Page	1
Ending Page	5
File Size	336021
Page Count	5
File Format	PDF
ISBN	9536037394
DOI	10.1109/ICECOM.2003.1290941
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-01
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electromagnetic devices Design engineering Power engineering computing Stochastic processes Electromagnetic forces Genetic engineering Particle swarm optimization Genetic algorithms Power engineering and energy Design optimization
Content Type	Text
Resource Type	Article

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