An improved snow leopard optimization algorithm is proposed to solve complex optimization problems,such as insufficient global exploration ability and low optimization accuracy.Firstly,the initial population diversity is improved based on piecewise Logistic chaotic mapping initialization.Secondly,a nonlinear scaling factor is introduced to balance the global exploration ability and local devel-opment ability of the algorithm.Then,a differential variation strategy is proposed.After the first population update position,five random individuals are used to improve the global search ability and the convergence ability of the algorithm.After the second population update position,three random individuals are used to ensure the global exploration ability in the middle and late period of the solution process,so as to avoid falling into the local optimal as much as possible.The proposed algorithm is tested on IEEE CEC2022 benchmark function test set and compared with other algorithms.The results show that the proposed algorithm has great improvement in population quality,solving accuracy and algorithm stability.Finally,the proposed algorithm is applied to engineering optimization,and the calculation results further confirm its strong optimization ability.
维普
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