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      PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

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          Comparison of multiobjective evolutionary algorithms: empirical results.

          In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.
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            Evolutionary programming made faster

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              Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II

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                Author and article information

                Journal
                IEEE Computational Intelligence Magazine
                IEEE Comput. Intell. Mag.
                Institute of Electrical and Electronics Engineers (IEEE)
                1556-603X
                November 2017
                November 2017
                : 12
                : 4
                : 73-87
                Article
                10.1109/MCI.2017.2742868
                9793ef46-d8fd-459b-8262-40871120abca
                © 2017
                Product
                Self URI (article page): http://ieeexplore.ieee.org/document/8065138/

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