On Analysis of Irregular Pareto Front Shapes

Shouyong Jiang*, Jinglei Guo, Bashar Alhnaity, Qingyang Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)


After decades of effort, evolutionary algorithms have been able to solve a variety of multiobjective optimisation problems with diverse characteristics. However, the presence of irregularity in the Pareto-optimal front is increasingly recognised as a big challenge to some well-established algorithms. In order to further our understanding of this irregularity and its effect on algorithms, we develop a generic framework of constructing irregular Pareto-optimal front shapes, and use it as a tool to examine the performance of some well-known algorithms. Experimental results reveal that conventional algorithms are not always inferior to the state of the arts, and all the algorithms considered in this paper face some unexpected challenges when dealing with irregularity of Pareto-optimal front. The findings suggest that a systematic evaluation and analysis is needed for any newly-developed algorithms to avoid biases.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings
EditorsHisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030720612
Publication statusPublished - 2021
Event11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 - Shenzhen, China
Duration: 28 Mar 202131 Mar 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12654 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by National Natural Science Foundation of China (Grant No. 62006103).


  • Evolutionary algorithm
  • Irregular Pareto front
  • Multi-objective optimization
  • Test problem


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