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A cooperative particle swarm optimizer with statistical variable interdependence learning

<24> This paper published in high impact journal proposes a novel cooperative particle swarm optimizer with statistical variable interdependence learning. A statistical model is used to explore interdependencies among variables and partition a problem into overlapping small sub-problems that are solved cooperatively. Theoretical analysis and results demonstrate the performance advantages over recent cooperative optimization algorithms. The work was conducted in collaboration with China and Japan. The approach and results provided a foundation for a successful funding application to The Wellcome Trust on medical data analysis.