# Open Competency Optimization: A Human-Inspired Optimizer for the Dynamic Vehicle-Routing Problem
## 4 Experimental Studies
### Comparison with Other Metaheuristics  
In the second set of experiments, the GDBG system was used, and the parameters of the test problems were set in the same way as in [9]. OCO was compared with SGA, the random-immigrant genetic algorithm (RIGA) [40] with a total number of immigrants of Ni = 30, and the hypermutation genetic algorithm (HMGA) [41]. It was also interesting to compare OCO with two recent methods published in the CEC 2009 competition. The first one was the clustering particle swarm optimizer (CPSO) [42], while the second was the differential ant-stigmergy algorithm (DASA) [43]. For the third experiment, the parameters of the SGA, HMGA, and RIGA were as follows: simulated binary crossover [38] with a crossover rate of 0.85. For the mutation, we considered Gaussian mutation with a mutation rate of 0.15. We used a tournament selection with a size of 4. The population size was N = 100. Tables 3-8 present the results of the best average values, mean average values, worst average values, and standard error (STD). The results allowed us to compare the quality of the solutions found by each technique. Table 3 shows that the performance of the four methods seemed to be almost similar, with a marked improvement in OCO, as F1 was the simplest to optimize. However, when the number of peaks was equal to 50, OCO outperformed the other techniques. Table 4 presents the results for a more complicated function, which had multiple optima and a dimension of 10. The SGA technique lost diversity due to the convergence of the algorithm. RIGA lost the quality of the solution due to the random individuals included in the population, and the diversity was much lower than that of OCO. Table 5 shows that for the function F3 that had multiple optima, the quality of the solution of OCO was much better, but it was interesting that, in this table, the diversity of the population in the case of OCO was much higher than that of the other  
Figure 5: Comparison of the diversity of OCO with that of four other algorithms. techniques. Table 6 shows the performance of each method for a complicated multi-modal function with a huge number of local optima. OCO used the dynamic population size and then assigned each new individual in the three steps of the proposed metaheuristic. So, OCO did not fall into the trap of local optima, and it was able to find the global optimum. Therefore, the other techniques (SGA, HMGA, RIGA) had a problem finding the global optimum and dove into the local optima, so the error values were high. The results for function F5 are presented in Table 7. The performance of OCO was much better than that of SGA, HMGA, and RIGA. Table 8 presents the results of the composition of complicated functions especially Weierstrass's function, which is continuous at all points in the search space but is not differential at any point. In this complicated function, OCO presented the best results compared with the other techniques. By observing the results in Table 9, it can be seen that the challenges of different change types were quite different. Looking at the difficulty of the problems starting from the simplest, the small step, and continuing to the most complicated test to optimize, SGA, RIGA, and RIGA have difficulties in optimizing the problems, especially chaotic changes and dimensional changes. However, in all tests, OCO performed much better than SGA, RIGA, and HMGA. It is interesting to take note that the overall performance of OCO was equal to 68.75, while that of CPSO was only equal to 57.57. Therefore, OCO outperformed CPSO because OCO used an equilibrium between exploitation and exploration. The performance of DASA, which was based on ant colonies, was much lower than that of OCO.