Reference Hub2
Design Optimization of Radar Absorbing Materials Using Particle Swarm Optimization

Design Optimization of Radar Absorbing Materials Using Particle Swarm Optimization

Kavya Kumari Sivakoti, Mamatha Basava, Rao Venkata Balaga, Balarama Murty Sannidhi
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 20
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522513247|DOI: 10.4018/IJAMC.2017100107
Cite Article Cite Article

MLA

Sivakoti, Kavya Kumari, et al. "Design Optimization of Radar Absorbing Materials Using Particle Swarm Optimization." IJAMC vol.8, no.4 2017: pp.113-132. http://doi.org/10.4018/IJAMC.2017100107

APA

Sivakoti, K. K., Basava, M., Balaga, R. V., & Sannidhi, B. M. (2017). Design Optimization of Radar Absorbing Materials Using Particle Swarm Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 8(4), 113-132. http://doi.org/10.4018/IJAMC.2017100107

Chicago

Sivakoti, Kavya Kumari, et al. "Design Optimization of Radar Absorbing Materials Using Particle Swarm Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 8, no.4: 113-132. http://doi.org/10.4018/IJAMC.2017100107

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Microwave absorbers have numerous applications in the modern-day military and civil industries. This paper presents the performance of the Particle Swarm Optimization (PSO) algorithm to obtain optimal designs for multilayer microwave absorber over different frequency ranges. The goal of this optimization is to make decision about number of layers, selection of suitable combination of materials from a predefined database, thereby minimizing the overall reflection coefficient and designing a low weight electromagnetic absorber, which absorbs the maximum amount of incident electromagnetic energy. Microwave absorbers or radar absorbing materials (RAM) performance is studied by varying thickness and number of layers. For each different configuration obtained with PSO, simulated results are presented. The best results obtained using PSO are compared with those obtained using another optimization technique, genetic algorithm and also compared with the results computed using standard RCS computation software.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.