The threats and challenges of unmanned aerial vehicle (UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is di?cult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved fi refl y algorithm that employs a neighborhood leaing strategy with a feedback mechanism and chaotic local search by elite fi refl ies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial fi refl y positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical fi refl y algorithm (FA) and four recently proposed FA variants.