An improved white shark optimizer algorithm used to optimize the structural parameters of the oil pad in the hydrostatic bearing

Abstract

Abstract An improved white shark optimizer (MWSO) algorithm has been proposed. The algorithm adopts an improved tent chaotic mapping strategy to enhance the diversity of the initial population of white sharks, introduces the balance pool strategy of the EO algorithm to improve the convergence speed and accuracy of the algorithm, applies adaptive t-distribution dynamic selection probability perturbation to the global optimal solution, and adjusts the exploration and development ability of the algorithm at different iteration periods. MWSO, WSO, and seven excellent metaheuristic algorithms are tested and compared on 23 classic test functions and the CEC2017 test suite, and two non-parametric tests, a Wilcoxon rank sum test with a significance level of 0.05 and Friedman test, are conducted. The statistical results indicate that the proposed MWSO is significantly superior to other algorithms. In addition, nine algorithms are applied for the first time to optimize the structural parameters of the oil sealing edge of oil pads in response to the issue of the bearing capacity of hydrostatic bearings. This not only further verified the superiority of MWSO, but also provided new ideas for the optimization of hydrostatic bearings.