A Multi-Strategy Hippopotamus Optimization Algorithm for Flexible Job Shop Scheduling Problems

The Flexible Job-shop Scheduling Problem (FJSP) is an important issue in the field of production scheduling. With the rise and integration of Industry 4.0, FJSP has attracted increasing attention. Meta-heuristic algorithms provide a powerful and adaptable method for handling complex discrete optimization problems and are widely used to solve FJSP. The Hippopotamus Optimization Algorithm(HO), a swarm intelligence optimization algorithm inspired by the behavior of hippos, has been proposed in recent years. By employing the good point set initialization method to increase the diversity of the population, a multi-strategy Hippopotamus Optimization Algorithm that combines the Sine Cosine Algorithm(SCA) and the opposition-based learning strategy has been proposed to balance the global and local search capabilities of the algorithm, effectively solving the FJSP problem. Finally, the algorithm is tested on the Brandimarte dataset, and the experimental results show that the multi-strategy Hippopotamus Optimization Algorithm(MSHO) has a faster convergence speed, better stability, and a wider range of applicability.