Dynamic Multi-objective Optimization for Coagulating Process of Carbon Fiber Precursor Based on Lifelong Learning

Abstract

Abstract Process parameter optimization is an essential link in the coagulating process of carbon fiber. In this paper, considering the dynamic factors of the coagulating bath environment, a dynamic multi-objective optimization problem (DMOP) model for the coagulating process is constructed with process parameters as decision variables and performance indicators as optimization objectives. We combine lifelong learning (LLL) and multi-objective optimization to solve this model and propose a lifelong learning-based dynamic multi-objective evolutionary algorithm (LLL-DMOEA). In LLL-DMOEA, the lifelong learning method is used to learn common knowledge in historical environments. After the arrival of the new environment, we use the common knowledge to generate a high-quality initial population and speed up the process of the multi-objective optimization algorithm to find the Pareto-optimal set (POS) in the new environment. At the same time, common knowledge only requires information in historical environments. The response time to environmental changes can be sped up by learning before the new environment arrives. Experimental results show that the proposed algorithm can effectively solve the optimization problem of process parameters in the coagulating process.