Pratyusha Rakshit
Papers
14
Total Citations
321
H-Index
7
About
Pratyusha Rakshit is a leading researcher in swarm intelligence, evolutionary multi-objective optimization, and multi-robot coordination. Her work bridges the gap between nature-inspired algorithms and real-world robotic applications, particularly in path planning, box-pushing, and stick-carrying tasks. Her most cited paper, “Multi-robot path-planning using artificial bee colony optimization algorithm” (101 citations), pioneered the use of the Artificial Bee Colony (ABC) algorithm for trajectory determination in multi-robot systems. She has made significant contributions to uncertainty management in optimization, with her 2013 paper on “Uncertainty Management in Differential Evolution Induced Multiobjective Optimization in Presence of Measurement Noise” (87 citations) introducing adaptive sample size strategies to handle noisy objective surfaces. Rakshit’s innovative hybrid algorithms, such as ABC-TDQL (14 citations) combining ABC with Q-learning, and her work on modified Imperialist Competitive and Bat algorithms, demonstrate her ability to enhance convergence and robustness. Her research on memory-based self-adaptive sampling (2019) further advances noisy multi-objective optimization. With over 300 total citations, Rakshit’s work is essential reading for researchers tackling complex, uncertain environments in robotics and evolutionary computation.
Research Focus
Key Achievements
Top Papers
- 1Multi-robot path-planning using artificial bee colony optimization algorithm101 citations · 2011
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- 5ABC-TDQL: An adaptive memetic algorithm14 citations · 2013
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- 10Multi-robot box-pushing in presence of measurement noise6 citations · 2016