Dwi Purnomo
Papers
2
Total Citations
14
H-Index
2
About
Dwi Purnomo’s research lies at the intersection of swarm intelligence and odor source localization (OSL), a field where robotic systems are trained to detect and trace chemical plumes to their origin. His work focuses on refining Particle Swarm Optimization (PSO) algorithms to accelerate and improve the accuracy of single and multiple odor source searches—a challenge with applications in environmental monitoring, hazardous leak detection, and search-and-rescue operations. In his highly cited 2016 paper, “PSO Algorithm for Single and Multiple Odor Sources Localization Problems: Progress and Challenge,” Purnomo systematically reviewed experimental approaches and identified key bottlenecks in odor sensing and localization. That same year, he introduced a novel modification to PSO in “Modification of Particle Swarm Optimization by Reforming Global Best Term to Accelerate the Searching of Odor Sources,” where he reformed the global best term to speed up convergence without relying solely on wind dynamics. With over 14 citations across his most prominent works, Purnomo’s contributions are recognized as foundational steps toward more efficient, autonomous robotic olfaction systems. His research continues to inspire new strategies for integrating swarm behavior with real-world environmental sensing.
Research Focus
Key Achievements
Top Papers
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