Illya Bakurov
Papers
1
Total Citations
4
H-Index
1
About
Illya Bakurov is a researcher whose work lies at the intersection of swarm intelligence, evolutionary computation, and autonomous systems. His most notable contribution, "PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming" (2018), introduces a novel framework that combines Particle Swarm Optimization (PSO) with Genetic Programming to enable aerial swarms to autonomously navigate and search in unknown, dynamic environments. This work is particularly impactful for applications in disaster response, environmental monitoring, and defense, where swarms must adapt to unseen vector fields without prior mapping. Although his citation count is still growing—with this paper garnering 4 citations—Bakurov's approach represents a significant step forward in making swarm algorithms more robust and scalable for real-world, unstructured scenarios. His research is characterized by a focus on practical, deployable solutions that bridge theoretical optimization with hardware constraints. For students and researchers in robotics, AI, and control systems, Bakurov’s work offers a compelling example of how evolutionary methods can be harnessed to solve complex, decentralized coordination problems in uncertain environments.
Research Focus
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Top Papers
- 1