Papers
14
Total Citations
400
H-Index
8
About
Seyedali Mirjalili is a prominent computational intelligence researcher whose work sits at the dynamic intersection of swarm intelligence, meta-heuristic optimization, and autonomous robotics. He is best known for developing and advancing nature-inspired algorithms, particularly the Sine Cosine Algorithm and Salp Swarm Algorithm, with his comprehensive survey of the former alone accumulating 178 citations, reflecting the field's strong engagement with his foundational contributions. His research consistently pushes the boundaries of evolutionary computation, as demonstrated through innovations like the fitness-based modified PSO (m-PSO) for robotic inverse kinematics and multi-objective optimization frameworks that adapt particle behavior rather than simply tuning algorithm parameters. Beyond algorithmic theory, Mirjalili applies these methods to real-world robotics challenges, including autonomous robot navigation via neuroevolution and multi-robot exploration of unknown environments, where hybrid approaches such as the Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME) method offer practical advances in coverage and coordination. His recent work on PID-controlled micro-robotics further demonstrates the breadth of his applied focus. With a body of work spanning foundational algorithm design to cutting-edge robotic systems, Mirjalili has established himself as a significant voice shaping modern bio-inspired computing research.
Research Focus
Key Achievements
Top Papers
- 1A comprehensive survey of sine cosine algorithm: variants and applications178 citations · 2021
- 2Neuroevolution-based autonomous robot navigation: A comparative study48 citations · 2020
- 3A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm47 citations · 2022
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- 6Autonomous Robot Navigation Using Moth-Flame-Based Neuroevolution19 citations · 2019
- 7Multi-objective Particle Swarm Optimization18 citations · 2019
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