Aiming at the shortcomings of the Runge Kutta optimization algorithm,such as slow convergence speed and easy to fall into local optimum,a multi-strategy improved Runge Kutta optimization algorithm is proposed.A hybrid opposition-based learning strategy is introduced to expand the optimization range of the population and enhance the search ability of the algorithm,then the Levy flight strategy is used to enhance the ability of the algorithm to jump out of the local optimum,and the dynamic adjustment factor is introduced to balance the exploitation and exploration ability of the algorithm more effectively.Finally,multi-dimensional numerical experiments are carried out on 15 benchmark functions and Wilcoxon rank-sum test is performed,and the experimental results show that the proposed algorithm has a better optimisation performance compared with the comparative algorithms.In addition,the test experiments on the welded beam design problem further verify the feasibility and effectiveness of multi-strategy improved Runge Kutta optimization algorithm on engineering problems.
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