Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm

The optimization of operating parameters for the simulated moving bed (SMB) is complex. A parameter optimization method for the SMB system was proposed based on the improved multi-objective sand cat swarm optimization (IMOSCSO) algorithm. The multi-objective sand cat swarm optimization (MOSCSO) algorithm integrated the update and selection mechanism of the repository in the multi-objective algorithm. Three strategies were proposed to improve the traditional MOSCSO algorithm for increased population diversity, global search capability, and convergence speed. First, the cubic chaotic map was used to initialize the population, which improved the uniformity of the population distribution. Second, including a variable spiral search strategy in the prey search phase enabled the sand cat swarm to explore more search paths to adjust its position. Third, the convergence speed was enhanced by incorporating the alert mechanism of the sparrow search algorithm. The improved algorithm was tested with standard test functions. The IMOSCSO algorithm outperformed other algorithms in terms of convergence and accuracy. Finally, the IMOSCSO algorithm optimized the system parameters of the SMB, demonstrating its practical applications.