Hyper-heuristics with penalty parameter adaptation for constrained optimization

Penalty functions are widely used in constrained optimization, but determining optimal penalty parameters or weights turns out to be a difficult optimization problem itself. The paper proposes a hyper-heuristic approach, which searches the optimal penalty weight setting for low-level heuristics, taking the performance of those heuristics with specialized penalty weight settings as feedback to adjust the high-level search. The proposed approach can either be used for merely improving low-level heuristics, or be combined into a common hyper-heuristic framework for constrained optimization. Experiments on a set of well-known benchmark problems show that the hyper-heuristic approach with penalty parameter adaptation is effective in both aspects.