On the cooperation of meta-heuristics for solving many-objective problems: An empirical analysis including benchmark and real-world problems

The performance of state-of-the-art evolutionary algorithms in solving many-objective problems varies ac-cording to different problem characteristics, which poses a challenge for many-objective optimization. In this study, we analyze the cooperative hyper-heuristic (HH-CO) for many-objective optimization. HH-CO tackles the challenge of dynamically finding the best MOEA (multi-objective evolutionary algorithm) for applying and, at the same time, exploiting the MOEAs cooperation for a given problem instance. This recently proposed hyper-heuristic (HH) showed results competitive to stand-alone MOEAs and a state-of-art hyper-heuristic. Our goal is to identify what leads HH-CO towards its competitive results and distinguishes it from other state-of -art hyper-heuristics. To answer those questions, we observed the choices made by HH-CO and a state-of-art HH. In addition, we analyzed how those choices are related to the quality of MOEAs applied stand-alone. Furthermore, we evaluated scenarios where HH-CO presented better and worse results and identified the main reasons for these outcomes. Overall, HH-CO presented better results in 80% of instances. We concluded that the greedy selection heuristic employed by HH-CO could be improved. Still, the positive influence of the cooperative migration procedure surpasses HH-CO deficiencies for most problem instances. Finally, we evaluated the capabilities of both strategies on a real-world problem. They achieved very similar hypervolume results, without a significant difference to the best MOEA, but better than some state-of-the-art MOEAs.

Automated summary

The cooperative hyper-heuristic HH-CO shows competitive results in solving many-objective optimization problems by utilizing a greedy selection heuristic and cooperative migration procedure, outperforming in 80% of instances. Comparison and analysis of choices made by HH-CO and other models reveal its effectiveness in utilizing MOEAs and distinguishing features that lead to successful outcomes.