Dynamic operation optimization based on improved dynamic multi-objective dragonfly algorithm in continuous annealing process

Abstract

The continuous annealing production process is the strip iron heat treatment process. Considering the numerous disturbance parameters and frequent environmental changes in the production process, dynamic multi-objective optimization is studied. (1)To anticipate whether the environment will change, this paper proposed dynamic detection based on Long-Short Term Memory (LSTM) prediction mechanism. A single-step prediction method is adopted to predict the strip hardness at the next moment which is compared with the value at the previous moment. (2)To track the dynamic Pareto frontier, the Information Fusion (IF) strategy in Dynamic Multi-Objective Dragonfly Algorithm (DMODA) is proposed. The population is updated by integrating the environmental offset with the evolution information. Computational experiments show the proposed strategy is effective when dealing with dynamic problems and it has been well applied in the continuous annealing process. The strip steel quality and production capacity can remain stable during the dynamic production process.