Research on assembly line scheduling based on small population adaptive genetic algorithm

Abstract

Based on the assembly line scheduling problem, an improved adaptive genetic algorithm is proposed to solve the problem that small population genetic algorithm is easy to fall into local optimal solution. In the improved genetic algorithm, the mutation rate is increased in the early iteration to improve the diversity of offspring, and the mutation rate is reduced in the later iteration to retain effective genes. The improved roulette selection method is used to solve the problem that value of optimization objectives is large and single change of it is small. In order to improve the local search ability and computational speed of the algorithm, an adaptive genetic operator is used to dynamically adjust the crossover operator in the evolution process. The feasibility of the small population adaptive genetic algorithm is verified by experiments, and the performance is compared.