Data-driven evolutionary algorithms based on initialization selection strategies, POX crossover and multi-point random mutation for flexible job shop scheduling problems

Abstract

In the fields of manufacturing and production, the precise solution of the flexible job shop scheduling problem (FJSP) is crucial for improving production efficiency and optimizing resource allocation. However, the complexity of FJSP often leads traditional optimization methods to face high computational costs and lengthy processing times. To address this problem, we propose a data-driven evolutionary algorithm based on initialization selection strategies, POX crossover, and multi-point random mutation (DDEA-PMI). This algorithm replaces the real objective function by constructing a radial basis function (RBF) surrogate model to reduce expensive computational costs and shorten solution time. In the process of solving FJSP, we use global selection (GS), local selection (LS), and random selection (RS) initialization selection strategies to obtain an initial population with high diversity. In order to reduce the generation of infeasible solutions, we use the POX crossover operator, which selects partial gene sequences from the parent generation and maps them to the offspring to preserve excellent features and ensure the feasibility of the solution. In addition, we design a multi-point random mutation operation to enhance the diversity of the population. Through the multi-point mutation strategy, it is able to explore more comprehensively in the solution space to increase the possibility of finding the optimal solution. To verify the effectiveness of DDEA-PMI, we compare it with three same types of data-driven evolutionary algorithms. We compare and analyze the DDEA-PMI with three algorithms after removing one of our proposed strategies. The experimental results show that DDEA-PMI is effective and has advantages in solving FJSP.