Hyper-heuristics with penalty parameter adaptation for constrained optimization

Abstract

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 lowlevel 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.

The ECL Optimization and Experiment of HSPMSM with Improved Method

“…Thus, the combination of those two approaches can be utilized in MOO of HSPMSM via improved algorithm and adjusted parameters. The constraints are often transformed to penalty functions added to the objective function based on particle swarm optimization (PSO) [25][26] and harmony search (HS) [27] algorithm.et [28][29].…”