Abstract not found

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 hyperheuristics, 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 hyperheuristic 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 di#erent 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.

The overall aim in multi-objective optimization is to aid the decisionmaking process when tackling multi-criteria optimization problems. In an a posteriori approach, the strategy is to produce a set of nondominated solutions that represent a good approximation to the Pareto optimal front so that the decision-makers can select the most appropriate solution. In this paper we propose the use of diversity measures to guide the search and hence, to enhance the performance of the multi-objective search algorithm. We propose the use of diversity measures to guide the search in two different ways. First, the diversity in the objective space is used as a helper objective when evaluating candidate solutions. Secondly, the diversity in the solution space is used to choose the most promising strategy to approximate the Pareto optimal front. If the diversity is low, the emphasis is on exploration. If the diversity is high, the emphasis is on exploitation. We carry out our experiments on a two-objective optimization problem, namely space allocation in academic institutions. This is a real-world problem in which the decision-makers want to see a set of alternative diverse solutions in order to compare them and select the most appropriate allocation. 1.

We investigate cooperative local search to improve upon known results of the office-spaceallocation problem in universities and other organizations. A number of entities (e.g., research students, staff, etc.) must be allocated into a set of rooms so that the physical space is utilized as efficiently as possible while satisfying a number of hard and soft constraints. We develop an asynchronous cooperative local search approach in which a population of local search threads cooperate asynchronously to find better solutions. The approach incorporates a cooperation mechanism in which a pool of genes (parts of solutions) is shared to improve the global search strategy. Our implementation is single-processor and we show that asynchronous cooperative search is also advantageous in this case. We illustrate this by extending four single-solution meta-heuristics (hill-climbing, simulated annealing, tabu search, and a hybrid meta-heuristic) to population-based variants using our asynchronous cooperative mechanism. In each case, the population-based approach performs better than the single-solution one using comparable computation time. The asynchronous cooperative meta-heuristics developed here improve upon known results for a number of test instances.

The thesis will be available as a pdf-file for downloading from the institute homepage on: www.er.dtu.dk