Multi-Objective Optimization of Tea Sorting Process Based on Particle Swarm Optimization Algorithm

The tea industry is an important sector of the global economy, and optimizing the sorting process to improve product quality and increase profits is crucial. Multi objective optimization of tea sorting process based on particle swarm optimization algorithm is a promising method to achieve this goal. This algorithm uses a population based search method to simulate the behavior of bee colonies in nature. It optimizes multiple objectives simultaneously, such as minimizing the number of defective leaves and maximizing throughput. This method can help tea manufacturers achieve the quality standards they want while maintaining high production efficiency. In addition, the algorithm has been proven to be effective in practical applications. It has been successfully applied in various industries, including manufacturing, logistics, and finance. Therefore, its application in the tea industry can significantly improve productivity and profitability. In a word, multi-objective optimization of tea sorting process based on particle swarm optimization algorithm is a promising method for tea industry to improve product quality and increase profits. Its effectiveness has been proven in other industries, making it an attractive choice for future research and development work. This article designs a tea sorting system based on machine vision, which solves the social problem of difficult and expensive tea screening, and has a certain promoting effect on the batch and automated production of tea.