abstract

A decision space variable topology feature visualization and clustering method for Dynamic Multi-objective Optimization

Authors:

Min Chen,

Yanyan ZhangAuthors Info & Claims

GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 85 - 86

https://doi.org/10.1145/3638530.3664085

Published: 01 August 2024 Publication History

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Abstract

We propose a dynamic multi-objective optimization algorithm for visualizing and clustering topological features of decision space variables. It maps all the obtained historical optimal solutions to the two-dimensional space through t-SNE, clusters the points, and selects a corresponding proportion of individuals from different clusters to form a new population. Throughout the search process, the topological characteristics of the reduced historical optimal solutions can be visually displayed. We conducted a preliminary experimental analysis on the standard test function to verify the effectiveness of the proposed method.

References

[1]

Kalyanmoy Deb, Udaya Bhaskara Rao N, and Sindhya Karthik. 2007. Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling. In International conference on evolutionary multi-criterion optimization. Springer, 803--817.

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[2]

Arisa Toda, Satoru Hiwa, Kensuke Tanioka, and Tomoyuki Hiroyasu. 2022. Visualization, Clustering, and Graph Generation of Optimization Search Trajectories for Evolutionary Computation Through Topological Data Analysis: Application of the Mapper. In 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 1--8.

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[3]

Yan Wu, Yaochu Jin, and Xiaoxiong Liu. 2015. A directed search strategy for evolutionary dynamic multiobjective optimization. Soft Computing 19 (2015), 3221--3235.

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Index Terms

A decision space variable topology feature visualization and clustering method for Dynamic Multi-objective Optimization
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2024

2187 pages

ISBN:9798400704956

DOI:10.1145/3638530

Chair:
Xiaodong Li,
Program Chair:
Julia Handl

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2024

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GECCO '24 Companion

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SIGEVO

GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion

July 14 - 18, 2024

VIC, Melbourne, Australia

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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