Abstract: Aiming at overcoming drawbacks of insufficient search efficiency and tendency to slip into local extremes of beluga optimization algorithm, an enhanced beluga whale optimization(EBWO)algorithm was proposed in this paper. First, a weight-based scramble beluga was included and applied to the algorithm's development phase to enrich the position updating technique, and a greedy mechanism was employed to select a better location and increase the quality of the understanding. Second, an adaptive Gaussian strategy was introduced to locally perturb the beluga in the whale falling phase, to make it adjusted to the vicinity of the optimal position to improve the convergence speed of the algorithm. Finally, a convex lens imaging learning strategy was used to carry out the information position after sharing. The comparative examination of the optimization of the ten benchmark test functions, the CEC2020 test set, and the Wilcoxon rank sum test revealed that EBWO's optimization speed and convergence accuracy had significantly improved. To test the EBWO algorithm's practicality and feasibility, it was applied to solve engineering design problems involving speed reducers and pressure vessels. It was discovered through experimental comparative analysis that the EBWO algorithm had a certain degree of superiority in solving actual optimization problems.

Key words: beluga whale optimization, scramble beluga, Gaussian variation, convex lens imaging learning, engineering optimization