Choice function based hyper-heuristics for multi-objective optimization

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work onmulti-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as nondeterministic move acceptance methods for multi-objective optimization. A well known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the nonaDr Maashi ([email protected]) works as an Assistant Professor at the Department of Computer Science, Faculty of Computer and Information Technology, University of Tabuk, Saudi Arabia. Preprint submitted to Applied Soft Computing October 8, 2014 deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic.