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.

[1]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[2]  Francisco Herrera,et al.  Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems , 2011, Soft Comput..

[3]  Ernesto Benini,et al.  Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms , 2003, Evolutionary Computation.

[4]  Hussein A. Abbass,et al.  Searching under Multi-evolutionary Pressures , 2003, EMO.

[5]  Enrique Alba,et al.  Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem , 2009, IEEE Transactions on Evolutionary Computation.

[6]  Hussein A. Abbass,et al.  Multiobjective optimization for dynamic environments , 2005, 2005 IEEE Congress on Evolutionary Computation.

[7]  Frank Neumann,et al.  Do additional objectives make a problem harder? , 2007, GECCO '07.

[8]  Jason M. Daida,et al.  Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[9]  Richard J. Duro,et al.  Real-Valued Multimodal Fitness Landscape Characterization for Evolution , 2010, ICONIP.

[10]  Eduardo Segredo,et al.  Multiobjectivisation of the Antenna Positioning Problem , 2011, DCAI.

[11]  Eduardo Segredo,et al.  Analysing the Robustness of Multiobjectivisation Approaches Applied to Large Scale Optimisation Problems , 2013, EVOLVE.

[12]  Kenneth DeJong,et al.  Parameter Setting in EAs: a 30 Year Perspective , 2007, Parameter Setting in Evolutionary Algorithms.

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[14]  Joshua D. Knowles,et al.  Multiobjectivization by Decomposition of Scalar Cost Functions , 2008, PPSN.

[15]  Gara Miranda,et al.  Metco: a Parallel Plugin-Based Framework for Multi-Objective Optimization , 2009, Int. J. Artif. Intell. Tools.

[16]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[17]  Eduardo Segredo,et al.  Parallel island-based multiobjectivised memetic algorithms for a 2D packing problem , 2011, GECCO '11.

[18]  Richard A. Watson,et al.  Reducing Local Optima in Single-Objective Problems by Multi-objectivization , 2001, EMO.