Research on solving multi-objective flexible workshop scheduling problem based on genetic ant colony algorithm

This article is based on the optimization idea of genetic algorithm for multi-objective flexible workshop scheduling. Firstly, the problem domain is described, and three objective functions including minimum cycle time are defined. Then, genetic algorithm is used to search for preliminary optimization solutions. During this period, information entropy strategy is used to adaptively adjust parameters to ensure population diversity; Subsequently, the ant colony algorithm is used for precise search, and pheromones are allocated based on the dominant relationship obtained from the search, ultimately obtaining the non dominated solution of the Pareto frontier. The experimental results show that the genetic ant colony algorithm has obvious improvement in efficiency and solution accuracy compared with the ordinary genetic algorithm. The innovation of this paper lies in the use of genetic algorithm to initialise the population, and then the use of ant colony algorithm to carry out local accurate search; as well as adding the concept of information entropy in the genetic algorithm, and the use of control information entropy is relatively stable to self-adaptively regulate the rate of variation and crossover rate, balancing the exploration and utilisation of the algorithm.