A Visualisation Method for Pareto Front Approximations in Many-objective Optimisation

Wu, Kai Eivind; Panoutsos, George

doi:10.1109/cec45853.2021.9504904

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Methods based on parallel coordinates involve a parallel coordinate plot [106] and heat map [107]. Methods for mapping distance relationships among solutions in a high‐dimensional space into a visible two‐dimensional space include self‐organizing map (SOM) [108]; interpretable SOM [109]; Sammon mapping, which is also known as Sammon projection [110]; Neuroscale [111]; Isomap [112]; RadViz [113]; multidimensional scaling [114]; principal component analysis [115]; t‐SNE [116]; ADVICE [117]; polar coordinate‐based visualization [102]; ProD [118]; and palette visualization (PaletteViz) [119]. They are often used to visualize not only high‐dimensional objective spaces but also high‐dimensional variable spaces and high‐dimensional combined objective and variable spaces.…”

Section: Visualization Of High‐dimensional Pareto Frontmentioning

confidence: 99%

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Satō

Ishibuchi

2023

IEEJ Transactions Elec Engng

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Population‐based evolutionary algorithms are suitable for solving multi‐objective optimization problems involving multiple conflicting objectives. This is because a set of well‐distributed solutions can be obtained by a single run, which approximate the optimal tradeoff among the objectives. Over the past three decades, evolutionary multi‐objective optimization has been intensively studied and used in various real‐world applications. However, evolutionary multi‐objective optimization faces various difficulties as the number of objectives increases. The simultaneous optimization of more than three objectives, which is called many‐objective optimization, has attracted considerable research attention. This paper explains various difficulties in evolutionary many‐objective optimization, reviews representative approaches, and discusses their effects and limitations. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Visualization Of High‐dimensional Pareto Frontmentioning

confidence: 99%

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Satō

Ishibuchi

2023

IEEJ Transactions Elec Engng

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High Dimensional Many Objective Optimisation through Diverse Creation and Categorisation of Reference Vectors

Panoutsos

2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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Enhancing Decision Making in MDO through Interactive Visual Analytics on the Web

Henriques-Gil,

Mejia Parra,

Simões

2023

The 28th International ACM Conference on 3D Web Technology

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A Visualisation Method for Pareto Front Approximations in Many-objective Optimisation

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Cited by 3 publications

References 23 publications

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

High Dimensional Many Objective Optimisation through Diverse Creation and Categorisation of Reference Vectors

Enhancing Decision Making in MDO through Interactive Visual Analytics on the Web

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