Abstract. The concept of a hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches. The hyperheuristic manages the choice of which lowerlevel heuristic method should be applied at any given time, depending upon the characteristics of the region of the solution space currently under exploration. We analyse the behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem. Results obtained show the effectiveness of our approach for this problem and suggest wider applicability of hyperheuristic approaches to other problems of scheduling and combinatorial optimisation.
Abstract. The term hyperheuristic was introduced by the authors as a high-level heuristic that adaptively controls several low-level knowledgepoor heuristics so that while using only cheap, easy-to-implement lowlevel heuristics, we may achieve solution quality approaching that of an expensive knowledge-rich approach. For certain classes of problems, this allows us to rapidly produce effective solutions, in a fraction of the time needed for other approaches, and using a level of expertise common among non-academic IT professionals. Hyperheuristics have been successfully applied by the authors to a real-world problem of personnel scheduling. In this paper, the authors report another successful application of hyperheuristics to a rather different real-world problem of personnel scheduling occuring at a UK academic institution. Not only did the hyperheuristics produce results of a quality much superior to that of a manual solution but also these results were produced within a period of only three weeks due to the savings resulting from using the existing hyperheuristic software framework.
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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