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

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          Most cited references32

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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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            A review of multiobjective test problems and a scalable test problem toolkit

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              Evolutionary Algorithms for Solving Multi-Objective Problems

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                Author and article information

                Journal
                IEEE Transactions on Evolutionary Computation
                IEEE Trans. Evol. Computat.
                Institute of Electrical and Electronics Engineers (IEEE)
                1941-0026
                1089-778X
                April 2009
                April 2009
                : 13
                : 2
                : 284-302
                Article
                10.1109/TEVC.2008.925798
                3fc5c58f-d288-4943-8337-592ce7e21cf7
                © 2009
                History

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