Operational maturity of biological control systems have fuelled theinspiration for a large number of mathematical and logical models for control,automation and optimisation. The human brain represents the most sophisticatedcontrol architecture known to us and is a central motivation for severalresearch attempts across various domains. In the present work, we introduce analgorithm for mathematical optimisation that derives its intuition from thehierarchical and distributed operations of the human motor system. The systemcomprises global leaders, local leaders and an effector population that adaptdynamically to attain global optimisation via a feedback mechanism coupled withthe structural hierarchy. The hierarchical system operation is distributed intolocal control for movement and global controllers that facilitate gross motionand decision making. We present our algorithm as a variant of the classicalDifferential Evolution algorithm, introducing a hierarchical crossoveroperation. The discussed approach is tested exhaustively on standard testfunctions as well as the CEC 2017 benchmark. Our algorithm significantlyoutperforms various standard algorithms as well as their popular variants asdiscussed in the results.
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