A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem

1. Experiment design for parameters settings in SPGA There are several parameters that may influence the performance of the algorithm. For example, the larger population size may find better solution quality but cost larger computational expense. When the number of sub-populations is larger, it may have better diversity. However, it may also be a trade-off that to reduce the number of generations. Moreover, the secondary crossover and mutation operator are also considered because it may provide better solution quality. The crossover rate and mutation rate are set to 0.9 and 0.1 respectively. The factors and treatments of these factors are as shown in Table 1.