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How to Read Many-Objective Solution Sets in Parallel Coordinates
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DOI：10.1109/MCI.2017.2742869.png)
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Abstract
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Rapid development of evolutionary algor ithms in handling many-objective optimization problems requires viable methods of visualizing a high-dimensional solution set. The parallel coordinates plot which scales well to high-dimensional data is such a method, and has been frequently used in evolutionary many-objective optimization. However, the parallel coordinates plot is not as straightforward as the classic scatter plot to present the information contained in a solution set. In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives. We hope that these observations could provide some guidelines as to the proper use of the parallel coordinates plot in evolutionary many-objective optimization.
Keywords:
EVOLUTIONARY ALGORITHMS
PART I
OPTIMIZATION
REDUCTION
SELECTION
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