An improved hybrid slap swarm algorithm for solving multi-objective distributed permutation flow shop scheduling

Considering the multi-objective distributed permutation flow shop scheduling problem, an improved hybrid salp swarm algorithm is proposed, whose optimization goal is to minimize the maximum processing time and delay time. The introduction of the spiral search mechanism and inertia weight into the position update method not only helps to improve the search efficiency of the algorithm, but also takes into account the balance between the global search and local search functions of the algorithm to improve the diversity. Due to the population and optimization ability of the algorithm, the integrated Pareto algorithm dominates the elite selection strategy, and adds a differential evolution mechanism in the selection phase to prevent the algorithm from falling into local optimality. By using benchmark examples to test the improved algorithm, it is confirmed that the proposed algorithm can effectively solve the multi-objective distributed permutation flow planning problem.