To address the imbalance between exploration and exploitation,slow convergence speed,premature convergence,and poor local optima escape in the Beluga Whale Optimization (BWO),an Improved BWO algorithm combining dimension-by-dimension Gaussian mutation(IBWO) is proposed.Firstly,a dynamic parameter strategy is adopted to adjust the balance factor,achieving a better balance between exploration and exploitation.Secondly,the current-to-rand differential mutation operator is introduced to enhance the algorithm's exploration capability.Then,elite leadership strategies are incorporated to accelerate the convergence speed.Finally,dimension-by-dimension Gaussian mutation is applied to the current optimal solution,based on the positions of both the current optimal and worst solutions,thereby enhancing the algorithm's ability to escape local optima.To validate the performance of the improved algorithm,it is compared with seven other metaheuristic algorithms on the Congress on Evolutionary Computation's (CEC) 2017 test set,the experimental results demonstrate that IBWO exhibits superior optimization capabilities compared to other algorithms.Applied IBWO to three engineering problems,IBWO shows promising performance in solving complex real-world optimization problems.