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Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results

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Evolutionary Multi-Criterion Optimization (EMO 2017)

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Multi-objective optimization with more than three objectives has become one of the most active topics in evolutionary multi-objective optimization (EMO). However, most existing studies limit their experiments up to 15 or 20 objectives, although they claimed to be capable of handling as many objectives as possible. To broaden the insights in the behavior of EMO methods when facing a massively large number of objectives, this paper presents some preliminary empirical investigations on several established scalable benchmark problems with 25, 50, 75 and 100 objectives. In particular, this paper focuses on the behavior of the currently pervasive reference point based EMO methods, although other methods can also be used. The experimental results demonstrate that the reference point based EMO method can be viable for problems with a massively large number of objectives, given an appropriate choice of the distance measure. In addition, sufficient population diversity should be given on each weight vector or a local niche, in order to provide enough selection pressure. To the best of our knowledge, this is the first time an EMO methodology has been considered to solve a massively large number of conflicting objectives.

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  1. 1.

    Also known as decomposition-based method, but here we use the terminology reference point based method without loss of generality.

  2. 2.

    Due to the page limit, the parallel coordinate plots are put in the supplementary file, which can be found in


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This work was partially supported by EPSRC (Grant No. EP/J017515/1).

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Correspondence to Ke Li .

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Li, K., Deb, K., Altinoz, T., Yao, X. (2017). Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham.

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