Analysing the Adaptation Level of Parallel Hyperheuristics Applied to Multiobjectivised Benchmark Problems

Evolutionary Algorithms (EAs) are one of the most popular strategies for solving optimisation problems. Several variants of EAs are seen to exist. They usually have several components and parameters which must be fixed. Therefore, one of the main drawbacks of EAs is the complexity of their parameter setting. Multiobjectivisation consists in the reformulation of mono-objective problems as multi-objective ones. Multiobjectivisation has been used in several fields as a mechanism to avoid premature convergence in local optima. However, since they usually introduce more components and parameters into the optimisation scheme, they hinder even more the parameter setting of an EA. A hyper heuristic can be viewed as a heuristic that iteratively chooses between a set of given low-level (meta)-heuristics in order to solve an optimisation problem. Hence, hyper heuristics have been used as an approach to facilitate the application of EAs. In this work, a parallel hyper heuristic is applied to a set of well-known optimisation benchmark problems. The contribution of the work is twofold. First, the adaptation level - amount of considered historical knowledge - of the hyper heuristic is analysed. Moreover, the contribution of considering multiobjectivisation inside the model is studied. Computational results show the benefits of parallel hyper heuristics and multiobjectivisation.