A great deluge based learning hyper-heuristic for multi-objective optimisation
Maashi, Mashael S. . 2012
Hyper-heuristics have drawn increasing attention from the research community in recent years. In this study
, we propose an extended choice function based hyper-heuristic for multi-objective optimisation based on the great deluge algorithm (GDA).
We employ a non-deterministic move acceptance strategy using the great deluge algorithm that accepts only improving moves and worsen moves
in limited spaces under the boundary condition. As our hyper-heuristic approach is designed for multi-objective optimisation,
D metric integrated with the GDA as a comparison tool. The rain speed parameter in GDA is set to different values in order to investigate the effectiveness
of this parameter on the quality of solutions. The experimental results demonstrate the proposed approaches with different speed rain setting perform
better than the original approach, and it shows that the speed rain settings are highly problems depended. All hyper-heuristic approaches
are tested on the Walking Fish Group test suite, a common benchmark for multi-objective optimisation.
