Hyperheuristic Approaches for Multiobjective Optimisation
Edmund Burke, Dario Landa-Silva, Eric Soubeiga · 2003
Introduction In multiobjective optimisation the aim is to find solutions that represent a compromise between the various (sometimes conflicting) criteria used to evaluate the quality of solutions. A solution x is said to be non-dominated with respect to a set of solutions S if there is no other solution in S that is, as good as x in all the criteria and better than x in at least one of the criteria. In Pareto optimisation the goal is to find a set of solutions that is representative of the whole trade-off surface, i.e. non-dominated solutions that are a good approximation to the Pareto optimal front [5]. The present work proposes the use of hyperheuristics to improve the ability of local search-based metaheuristics to produce non-dominated fronts that are uniformly distributed over the desired trade-off surface. A hyperheuristic can be thought at as, basically, a heuristic that manages the application of a set of heuristics in order to solve an optimisation problem [1]. By using a hyp