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Abstract

The Red-billed Blue Magpie Optimization (RBMO) algorithm suffers from issues such as low convergence accuracy and an imbalance between global exploration and local exploitation capabilities. To address these challenges, this paper proposes an Advanced Red-billed Blue Magpie Optimization (ARBMO) algorithm that integrates multiple strategies. Firstly, dynamic parameters are employed to dynamically adjust the search step size, enhancing the algorithm's local search capability. Secondly, to improve the algorithm's global search efficiency, Gaussian noise is randomly added to some individuals during the food-searching phase (exploration phase), aiming to redistribute the population when the algorithm gets trapped in local optima. To accelerate convergence, elite preservation and replacement mechanisms are introduced in both the food-searching and prey-attacking phases of the RBMO algorithm, ensuring that the optimal individuals are not lost due to random perturbations while also improving the overall quality of the population. Numerical experiment results verify the effectiveness of the improved algorithm. Finally, the ARBMO is applied to the production scheduling problem of the stator assembly line for electric vehicles.