Abstract
The performance of multi-objective evolutionary algorithms (MOEA) is severely deteriorated when applied to many-objective problems. For Pareto dominance based techniques, available information about optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms (NSGA-II, SMPSO and GDE3) when corner solutions are inserted into the population at different evolutionary stages. Corner solutions are found using specific algorithms. Preliminary results are presented concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5).
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Freire, H., de Moura Oliveira, P.B., Solteiro Pires, E.J., Bessa, M. (2014). Corner Based Many-Objective Optimization. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_10
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DOI: https://doi.org/10.1007/978-3-319-01692-4_10
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