*5.1. Preliminary Tests*

In the preliminary experiment, the design of experiments (DOE) was conducted to decide the optimal parameter settings for the sAIS optimization. The size of the population and escape list were acquired through the experimentation methodology. One randomly selected instance from the 60 testing problems was used to execute the experiment methodology. The parameter of population size, after an initial trial-and-error process, was located in the interval of [100, 500], and we used the set of {100, 200, 300, 400, and 500} to run the single factor factorial design experiment at 5 levels. Figure 10 shows the mean plot of the population size factor from the results of the experiments. The variable *y* is the objective function's minimized value (fitness value), as is the *y*-axis in Figures 10 and 11. 0 DLQ ( IIHFWV3 OR WIR U\

**Figure 10.** The mean plot of population size for a one-factor factorial design.

**Figure 11.** The mean plot of the escape list for a one-factor factorial design.

When the length of the escape list is attained, the AIS algorithm will jump out of the present field. The DOE also determined the length from large numbers in the interval [300, 700], and we chose {300, 400, 500, 600, and 700} as the 5 levels for the single factor factorial design experiment. Figure 8 shows the mean plot of the escape list factor from the results of the experiment.

It is found that the sAIS method can generate a better-quality solution when the population size and escape list factors are assigned to 300 and 500. According to the literature review, the maximum iteration was set at 100,000 trials as the termination criterion in our experiments. These settings were used in the following experiment for the sAIS algorithm. On the other hand, the population size of genes in the GA method was set at 50; the mutation rate is 0.06; and the crossover rate is 0.15. The maximum iteration number was 5000 trials with 30 replications for each instance.
