Multiobjective particle swarm optimization with direction search and differential evolution for distributed flow-shop scheduling problem

As a classic problem of distributed scheduling, the distributed flow-shop scheduling problem (DFSP) involves both the job allocation and the operation sequence inside the factory, and it has been proved to be an NP-hard problem. Many intelligent algorithms have been proposed to solve the DFSP. However, the efficiency and quality of the solution cannot meet the production requirements. Therefore, this paper proposes a bi-objective particle swarm optimization with direction search and differential evolution to solve DFSP with the criteria of minimizing makespan and total processing time. The direction search strategy explores the particle swarm in multiple directions of the Pareto front, which enhances the strong convergence ability of the algorithm in different areas of Pareto front and improves the solution speed of the algorithm. The search strategy based on differential evolution is the local search strategy of the algorithm, which can prevent the multiobjective particle swarm optimization from converging prematurely and avoid falling into local optimum, so that a better solution can be found. The combination of these two strategies not only increases the probability of particles moving in a good direction, but also increases the diversity of the particle swarm. Finally, experimental results on benchmark problems show that, compared with traditional multiobjective evolutionary algorithms, the proposed algorithm can accelerate the convergence speed of the algorithm while guaranteeing that the obtained solutions have good distribution performance and diversity.