A Synergetic Immune Clonal Selection Algorithm Based Multi-Objective Optimization Method for Carbon Fiber Drawing Process

Carbon fiber production is a large-scale system which comprises a large number of production processes. Among the various complex production conditions, the drawing process is one of the most influential factors that affect the quality of carbon fiber. How to obtain the fittest process parameters of the drawing process is a typical multi-objective optimization problem. To address the drawbacks of mathematical programming techniques available for solving optimization problems, we propose a new synergetic immune clonal selection algorithm (SICSA) to obtain the optimal process parameters, such as the linear density, strength, and breaking elongation ratio. The main operators of the SICSA are synergetic evolution, clonal operation and non-uniform mutation. The synergetic evolution between populations adopts a "division-parallel-recombination" mode, the clonal operation searches for optimal solutions globally, and the non-uniform mutation explores optimal solutions locally and enhances the diversity of the solution's. As a result, optimal solutions which lead to reasonable distribution of the drawing ratio are obtained. We also compare the proposed SICSA with an immune algorithm and a genetic algorithm for optimizing the parameter in the drawing process. Our results show that the SICSA has the best performance in precision and convergence time. These results can serve as references and provide guidance for real production of carbon fiber.