%0 Journal Article %A HUI Lichuan %A CHEN Xuelian %A MENG Sibo %T Improved Sparrow Search Algorithm Based on Multi-Strategy Mixing %D 2022 %R 10.3778/j.issn.1002-8331.2202-0134 %J Computer Engineering and Applications %P 71-83 %V 58 %N 16 %X Dedicated to tackling the shortcomings of the simple sparrow search algorithm(SSA) with inadequate search area, sluggish convergence speed and convenient to crumple into partial top of the line when dealing with complicated optimization problems, an improved sparrow search algorithm based on multi-strategy mixing(IMSSA) is proposed. The sparrow individual position is initialized by the usage of Sine chaotic map, which enriches the vary of the population and compensates for the uneven population distribution and inadequate search space. The diversity global optimal guidance strategy with inertia weight is adopted to promote the convergence speed and regulate the overall search and local exploitation ability of the algorithm. The double-sample learning strategy is used which enables the algorithm soar out of the local optimum and enhance the population’s search capability of the solution space. The algorithm is simulated via test functions, and the effectiveness of three improved strategies is verified, as well as Wilcoxon rank sum test and time complexity evaluation have been carried out. The effects point out that the overall performance of IMSSA is notably improved. Finally, the algorithm is used to optimize the parameters of support vector machine and establish the bearing fault diagnosis model which confirms the validity of the modified strategy. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2202-0134