Solving the flexible job-shop scheduling problem based on the hierarchical collaborative evolution multi-objective artificial raindrop algorithm
Year: 2026
Type: preprint
Abstract: In the multi-objective flexible job shop scheduling problem, makespan, total energy consumption, and total cost are mutually conflicting and therefore difficult to optimize simultaneously. To address these limitations, this study proposes a hierarchical co-evolutionary multi-objective artificial raindrop algorithm (HCMOARA). Latin hypercube sampling is first used to initialize the population and improve the uniformity of the initial solution distribution. The population is then partitioned into exploration, exploitation, and basic-update subpopulations according to nondominated rank and crowding distance. DE-best-2 global search, SBX–RHC local search, and an adaptive flow-factor update strategy are applied to the three subpopulations, respectively, to enhance both global exploration and local exploitation. In addition, an external Pareto archive is employed to retain high-quality nondominated solutions. HCMOARA is evaluated on the Brandimarte benchmark instances MK01–MK10 and compared with several representative algorithms. The results demonstrate that HCMOARA achieves strong overall performance in terms of Pareto-set quality, makespan optimization, inverted generational distance (IGD), and hypervolume (HV). Ablation experiments further confirm the effectiveness of the proposed improvement strategies,indicating that HCMOARA is an effective approach for multi-objective flexible job shop scheduling.
Source: Research Square
Institution: Hebei University of Engineering
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204482592