An Improved Multi-Objective Adaptive Human Learning Optimization Algorithm and Its Application in Optimizing Formulation Schemes for Rotary Hearth Furnaces

This study develops a complex, multivariable optimization framework for raw material formulation in rotary hearth furnaces (RHF), addressing the inherent coupling effects among compositional control, operational stability, and resource efficiency. Based on an in-depth analysis of the multiple constraints that must be balanced in the blending process, this paper constructs a multi-objective optimization mathematical model incorporating elemental content, scheme similarity, a continuous operation time, and start–stop switching. An improved multi-objective adaptive human learning optimization algorithm (IMOAHLO) is proposed, which enhances local optimization through neighborhood search and an adaptive learning mechanism. This approach overcomes the shortcomings of traditional methods that rely on human expertise and are prone to getting trapped in local optima, ensuring the system operates stably over the long term while meeting production demands. Using 100 factual datasets from a steel plant’s RHF production line, comparative experiments between IMOAHLO and three other algorithms show that the proposed method outperforms its counterparts on three evaluation metrics: hypervolume, inverted generational distance, and generational distance. This indicates significant improvements in system stability, reduced operational fluctuations, and optimized elemental content in the blended materials. Furthermore, two practical case studies are presented to demonstrate the optimization results of the proposed algorithm under varying production conditions, proving its flexibility and high performance in multi-objective optimization applications in complex industrial scenarios and highlighting its significant engineering value. Although this work focuses on the RHF blending line in the steel industry, the same framework can be readily extended to other continuous blending processes.