Investigating collaboration methods of random immigrant scheme in cooperative coevolution

Previous study shows that using a random immigrant scheme in a cooperative coevolutionary algorithm (RI-CCEA) can significantly track the moving peaks in dynamic optimization. In this paper, we further investigate its behavior in the multi-modal environments where peak locations, peak coverage and peak heights of the moving peaks are changing during the course of optimization. Of the particular interest to us is the different combinations of the collaboration methods used by the original individuals and the RI individuals of the CCEA populations. Empirical comparisons show that in the moderate-changing or slow-changing environments, using the best collaborations in original individuals in the RI-CCEA outperforms other variants in our experiments, while the choice of the collaboration methods in RI individuals is insignificant. In a fast-changing environment, using the random collaborations in original individuals is crucial to achieve a better performance and the choice of the collaboration methods in RI individuals is also significant.