Comparative study of broadcasting antenna array optimization using evolutionary algorithms

Pavlos I. Lazaridis, Emmanouil N. Tziris, Zaharias D. Zaharis, Thomas D. Xenos, Violeta Holmes, John P. Cosmas, Ian Glover

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Broadcasting antenna array optimized design involves gain maximization, main lobe down-tilting and null filling. In this study some of the most powerful evolutionary optimization algorithms are applied to this challenging problem: Differential Evolution, Particle Swarm, Invasive Weed, Adaptive Invasive Weed, and the Taguchi method. Evolutionary algorithms use a random search approach together with mechanisms inspired by biological evolution in order to iteratively improve the precision of randomly obtained solutions. Evolutionary algorithms are shown to require very substantial computational resources due to their random search nature. However, they are also very robust in finding a quasi-optimum solution by optimizing an appropriate fitness function. It is demonstrated that the algorithm producing the best fitness, and thus the best solution to the antenna problem, is Invasive Weed Optimization (IWO), followed by Particle Swarm Optimization (PSO) and Differential Evolution (DE), in second place and with similar results.

Original languageEnglish
Title of host publication2016 URSI Asia-Pacific Radio Science Conference, URSI AP-RASC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages3
ISBN (Electronic)9781467388016
Publication statusPublished - 20 Oct 2016
Event2016 URSI Asia-Pacific Radio Science Conference - Grand Hilton Seoul Hotel, Seoul, Korea, Republic of
Duration: 21 Aug 201625 Aug 2016 (Link to Conference Programme )


Conference2016 URSI Asia-Pacific Radio Science Conference
Abbreviated titleURSI AP-RASC 2016
Country/TerritoryKorea, Republic of
Internet address


Dive into the research topics of 'Comparative study of broadcasting antenna array optimization using evolutionary algorithms'. Together they form a unique fingerprint.

Cite this