An Improved Snake Optimizer(ISO)of multi-strategy is proposed to address the limitations of the Snake Optimizer(SO)in exploration strategy,variable computation,space searching and population updating.Firstly,an optimized exploration strategy is proposed,where individuals update their positions based on their relative positions to the best individual.This allows the population to quickly converge to the vicinity of the optimal solution in the early stage.Secondly,variable computation is optimized by replacing the exponential operations in the SO with polynomial operations to improve the time efficiency of SO.Additionally,we introduce the dynamic adjustment mechanism of the search space,gradually expanding the search range with the increase in population evolution iterations to enhance the optimization capability.Finally,an advantage evolution strategy is introduced,which eliminates individuals with lower fitness and combines the genes of dominant individuals to generate new individuals.This strategy accelerates convergence by rapidly increasing the proportion of dominant genes in the population.Experiments were conducted on different benchmark test functions,comparing ISO with the classical SO and five heuristic algorithms.The results demonstrate that ISO exhibits strong optimization capabilities.To further validate the efficiency and practicality of the proposed algorithm,ISO is applied to the optimization problem of fully connected neural networks.The results show that neural networks optimized based on ISO achieve superior classification perform-ance.
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