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Analysing the robustness of multiobjectivisation parameters with large scale optimisation problems

Evolutionary Algorithms (EAs) are one of the most popular strategies for solving optimisation problems. To define a configuration of an EA several components and parameters must be specified. Therefore, one of the main drawbacks of EAs is the complexity of their parameter setting. Another problem is that EAs might have a tendency to converge towards local optima for many problems. For this reason, several methods to deal with local optima stagnation have been designed. Multiobjectivisation, which consists in the reformulation of mono-objective problems as multi-objective ones, is one of such methods. Some multiobjectivisation methods require the specification of parameters by the user. In some cases, the quality of the obtained solutions has been improved by these methods. However, they usually introduce more components and parameters into the optimisation scheme. The main contribution of this work is to deeply analyse the robustness of multiobjectivisation approaches with parameters. Several large scale continuous optimisation problems have been multiobjectivised in order to perform such a study. Extracted conclusions might allow designing methods which profit from multiobjectivisation with parameters, without incorporating additional parameters to the whole optimisation scheme. By this way, the parameter setting could be performed in an easier way. The experimental evaluation has provided promising results.