Optimizing Electromagnetic Cigarette Heaters Using PSO-NSGA II Algorithm: An Effective Strategy to Improve Temperature Control and Production Rate

Abstract

ABSTRACT The global tobacco industry has made significant strides in reducing the harmful emissions of tobacco by focusing on the development of non-burning cigarette products. Among these innovations, electrically heated noncombustible cigarette smoking sets have garnered attention. However, one of the challenges faced by these products is the reliance on empirical values for temperature control, resulting in subpar taste and low production rates. To address these issues, this research introduces an optimization strategy that utilizes advanced algorithms such as Particle Swarm Optimization (PSO) and traditional non-dominated sorting genetic algorithm-II (NSGA-II). By leveraging these algorithms, this study aims to optimize the performance of electrically heated noncombustible cigarette smoking sets. The research methodology encompasses a comprehensive review of relevant literature and the systematic introduction of a simulation method. Through the proposed approach, a finite element model verification is conducted, which demonstrates a minimal maximum relative error between the model values of the objective function and the simulation values of the optimized parameters. As a result, this multi-objective optimization approach proves to be highly effective in enhancing the performance of electromagnetic cigarette heaters. It not only addresses the taste and production rate issues associated with existing electrically heated noncombustible cigarette smoking sets but also provides a scientifically grounded method for optimizing these devices. This research paves the way for further advancements in the tobacco industry, contributing to the development of safer and more satisfying alternatives to traditional cigarettes.