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About

Swagatam Das is a computational intelligence researcher whose work sits at the intersection of evolutionary optimization, swarm intelligence, and autonomous robotics. His most significant contributions span multi-robot path planning, multiobjective optimization, and adaptive metaheuristic algorithms, making him a versatile figure in applied artificial intelligence. Das has made notable strides in robot navigation, developing hybrid approaches that combine swarm-based methods with reinforcement learning. His PSO-DV hybrid algorithm for multi-robot path planning in cluttered environments (136 citations) and his Firefly Algorithm–Q-Learning synergy for robot arm planning (92 citations) demonstrate a consistent talent for fusing bio-inspired search strategies with machine learning to solve real-world robotics challenges. In optimization theory, his 2013 work on uncertainty management in multiobjective differential evolution (87 citations) introduced adaptive sampling strategies to achieve reliable Pareto-optimal solutions under noisy conditions — a technically demanding problem with broad practical relevance. His formulation of the box-pushing problem through NSGA-II further illustrates his creativity in reframing classical robotics tasks as principled multiobjective problems. More recently, Das has explored manifold-based metaheuristics and robust deep reinforcement learning, signaling an ambitious expansion toward geometry-aware and resilient AI systems. His body of work reflects a researcher continuously bridging foundational optimization theory with cutting-edge autonomous systems research.