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About

Bahareh Nakisa is a researcher specializing in swarm intelligence, multi-robot systems, and computational optimization. Her work centers on applying and refining Particle Swarm Optimization (PSO) algorithms to solve complex real-world challenges, particularly in autonomous robot navigation and target searching within dynamic and obstacle-laden environments. Nakisa's most significant contributions lie in developing hybrid PSO frameworks that strategically balance exploration and exploitation — a critical challenge in optimization research. Her 2015 paper introducing a hybrid of modified PSO and local search on multi-robot systems stands as her most influential work, garnering 30 citations, and demonstrates her ability to address PSO's well-known limitations such as premature convergence and local minima trapping. Her complementary multi-swarm PSO approach, which employs multi-best particles across cooperative robot teams, further illustrates her innovative problem-solving methodology. Beyond algorithm development, Nakisa has contributed valuable theoretical grounding to the field through her critical literature review of robot path planning in dynamic environments, providing researchers with a structured understanding of existing techniques and their limitations. With a cumulative citation count exceeding 80 across her key publications, her work continues to inform researchers working at the intersection of artificial intelligence, robotics, and evolutionary computation.