A multi-objective hyper-heuristic based on choice function

• A learning selection hyper-heuristic is proposed for multi-objective optimization.
• A choice function utilized within the framework for multi-objective optimization.
• Three MOEAs (NSGAII, SPEA2, and MOGA) are mixes and exploited their strengths.
• The proposed method performs better than three MOEAs and some other approaches.
• The proposed method is tested on a generic benchmark and a real-world problem.

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.