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Many-objective optimization with corner-based search

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Abstract

The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some 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 [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.

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Acknowledgments

This work was supported by the Fundação para a Ciência e a Tecnologia (FCT) under PhD studentship No. SFRH/BD/79463/2011.

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Authors and Affiliations

  1. Department of Engineering, School of Sciences and Technology, INESC TEC - INESC Technology and Science (formerly INESC Porto, UTAD pole) Universidade de Trás-os-Montes e Alto Douro, UTAD, Quinta dos Prados, 5001-801, Vila Real, Portugal

    Hélio Freire, P. B. de Moura Oliveira, E. J. Solteiro Pires & Maximino Bessa

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  1. Hélio Freire
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  2. P. B. de Moura Oliveira
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  3. E. J. Solteiro Pires
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  4. Maximino Bessa
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Correspondence to Hélio Freire.

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Freire, H., de Moura Oliveira, P.B., Solteiro Pires, E.J. et al. Many-objective optimization with corner-based search. Memetic Comp. 7, 105–118 (2015). https://doi.org/10.1007/s12293-015-0151-4

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