Application of Beetle Colony Optimization Based on Improvement of Rebellious Growth Characteristics in PM2.5 Concentration Prediction

Abstract

Aiming at the shortcomings of the beetle swarm algorithm, namely its low accuracy, easy fall into local optima, and slow convergence speed, a rebellious growth personality–beetle swarm optimization (RGP–BSO) model based on rebellious growth personality is proposed. Firstly, the growth and rebellious characters were added to the beetle swarm optimization algorithm to dynamically adjust the beetle’s judgment of the optimal position. Secondly, the adaptive iterative selection strategy is introduced to balance the beetles’ global search and local search capabilities, preventing the algorithm from falling into a locally optimal solution. Finally, two dynamic factors are introduced to promote the maturity of the character and further improve the algorithm’s optimization ability and convergence accuracy. The twelve standard test function simulation experiments show that RGP–BSO has a faster convergence speed and higher accuracy than other optimization algorithms. In the practical problem of PM2.5 concentration prediction, the ELM model optimized by RGP–BSO has more prominent accuracy and stability and has obvious advantages.