An important issue in multi-objective optimisation is how to ensure that the obtained non-dominated set covers the Pareto front as widely as possible.A number of techniques (e.g. weight vectors, niching, clustering, cellular structures, etc.) have been proposed in the literature for this purpose. In this paper we propose a new approach to address this issue in multi-objective combinatorial optimisation. We explore hyper-heuristics, a research area which has gained increasing interest in recent years. A hyper-heuristic can be thought of as a heuristic method which iteratively attempts to select a good heuristic amongst many. The aim of using a hyper-heuristic is to raise the level of generality so as to be able to apply the same solution method to several problems, perhaps at the expense of reduced but still acceptable solution quality when compared to a tailor-made approach. The key is not to solve the problem directly but rather to (iteratively) recommend a suitable heuristic chosen because of its performance. In this paper we investigate a tabu search hyper-heuristic technique. The idea of our multi-objective hyper-heuristic approach is to choose, at each iteration during the search, the heuristic that is suitable for the optimisation of a given individual objective. We test the resulting approach on two very different real-world combinatorial optimisation problems: space allocation and timetabling. The results obtained show that the multi-objective hyper-heuristic approach can be successfully developed for these two problems producing solutions of acceptable quality.

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