Abstract:

In order to solve the scheduling optimization problem in mixed-model assembly lines, a multi-objective vaccine eoevolution elonal selection algorithm is proposed, and the vaccine population and the corresponding popu- lation operations are designed to interact and coevolve with the antibody population, thus greatly improving the per- formance of the algorithm. Then, according to the discrete feature of the scheduling optimization problem, the anti- body affinity is evaluated from the phenotype and the genotype. Moreover, according to the antibody quality and the evolutionary generations, the adaptive mutation rate is designed, and multiple local optimizations are executed in each iteration process to improve the convergence rate of the algorithm. The results of two series of experiments show that, as compared with other three multi-objective optimization algorithms, the proposed algorithm is of high efficiency and superiority.

Key words: mixed-model assembly line, multi-objective optimization, vaccine, coevolution, clonal selection