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Multiobjective Optimization Using Age-MOEA-II with Improved Environmental Selection

  • Chia Tzu Chang*
  • , Thammarsat Visutarrom
  • , Tsung Che Chiang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper aims to develop a multiobjective evolutionary algorithm (MOEA) to tackle multiobjective optimization problems with various Pareto front geometries. We propose an improved version of AGE-MOEA-II by incorporating a hyper-dominance-based filtering mechanism to promote convergence and an adaptive distance metric that balances geodesic and parallel distances to enhance diversity. Extensive experiments on the MaF test suite demonstrate that I-AGE-MOEA-II achieves superior performance compared to several state-of-The-Art MOEAs. Additional component-wise analysis confirms the effectiveness of the proposed improvements. These results suggest that the proposed I-AGE-MOEA-II algorithm offers a robust and scalable solution for handling complex MOPs.

Original languageEnglish
Title of host publicationProceedings of the 2025 International Conference on Machine Intelligence and Nature-Inspired Computing, MIND 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages396-402
Number of pages7
ISBN (Electronic)9798331587680
DOIs
Publication statusPublished - 2025
Event2025 International Conference on Machine Intelligence and Nature-Inspired Computing, MIND 2025 - Xiamen, China
Duration: 2025 Oct 312025 Nov 2

Publication series

NameProceedings of the 2025 International Conference on Machine Intelligence and Nature-Inspired Computing, MIND 2025

Conference

Conference2025 International Conference on Machine Intelligence and Nature-Inspired Computing, MIND 2025
Country/TerritoryChina
CityXiamen
Period2025/10/312025/11/02

Keywords

  • evolutionary algorithm
  • geometry estimation
  • multiobjective

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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