An adaptive dual-population based evolutionary algorithm for industrial cut tobacco drying system

Abstract

Industrial cut tobacco drying is one of the most important processes in cigarette production, which affects the taste, cut tobacco consumption and other indicators of cigarette products. Due to the complicated process and parameters involved, the production of drying system is difficult to improve. In this paper, the model of tobacco drying system is established and optimized. First, an eighth-order nonlinear first-principle model is established, and its corresponding constrained multi-objective optimization problem is constructed based on the multiple requirements in industrial production. Furthermore, an adaptive dual-population based evolutionary algorithm (ADPEA) is proposed in which an assistant population is introduced to balance the feasibility, diversity and convergence. Feasible solutions are preferentially reserved to the next generation in the main population, while diversity and convergence are considered more in the assistant population. The ADPEA is used to optimize the tobacco drying system and is compared with four state-of-the-art multi-objective evolution algorithms. The experimental results reveal that ADPEA has a better performance, and the optimization results could help engineers adjust the process parameters according to the requirements of different batches and brands of cigarette products to ensure that the whole production process can meet the technological requirements while saving energy and reducing emissions.