A multi-objective hyper-heuristic based on choice function
Maashi, Mashael S. . 2014
Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an
attempt to solve difficult computational optimization problems. We present a learning selection choice
function based hyper-heuristic to solve multi-objective optimization problems. This high level approach
controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e.
NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed
learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark
for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle
crashworthiness design problem as a real-world multi-objective problem. The experimental results
demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of
each low level heuristic run on its own, as well as being compared to other approaches including an adaptive
multi-method search, namely AMALGAM
