An improved variable weight Komodo optimization algorithm and its application
The Komodo Mlipir algorithm(KMA)is prone to premature convergence when solving complex functions and high dimensions.Therefore,an improved variable weight Komodo optimization algorithm(VWCKMA)is proposed.Firstly,the sequence generated by Tent chaos mapping is used to initialize the position of Komodo individuals,laying the foundation for the diversity of global search.Secondly,variable inertial weights are proposed to control the movement of Komodo individuals of different social classes respectively,which improves the convergence speed.Finally,the Tent chaotic mapping is used for local perturbation,so that it can conduct more accurate local searches and avoid local optimal values.The simulation experiments show that VWCKMA has greatly improved the convergence accuracy and convergence speed in the standard deviation and mean of solving unimodal functions and multimodal functions.Aiming to the issue of nonlinearity prediction of actual air pollutants PM2.5,VWCKMA is used to iteratively optimize the weights and thresholds of the BP neural network,and the BP neural network is used to predict PM2.5 based on the optimal parameters.The experimental results show that the prediction accuracy is 85.085％,which is 19.85 percentage points higher than the prediction accuracy of BP neural network,indicating that VWCKMA has certain practical application value.
万方数据
维普
