When to Truncate the Archive? On the Effect of the Truncation Frequency in Multi-Objective Optimisation

• URL: http://arxiv.org/abs/2504.01332v2
• Date: Sun, 13 Apr 2025 10:09:55 GMT
• Title: When to Truncate the Archive? On the Effect of the Truncation Frequency in Multi-Objective Optimisation
• Authors: Zhiji Cui, Zimin Liang, Lie Meng Pang, Hisao Ishibuchi, Miqing Li,
• Abstract summary: We show that, interestingly, truncating the archive once a new solution generated tends to be the best, whereas considering an unbounded archive is often the worst.<n>Our results highlight the importance of developing effective subset selection techniques.
• Score: 6.391724105255245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using an archive to store nondominated solutions found during the search of a multi-objective evolutionary algorithm (MOEA) is a useful practice. However, as nondominated solutions of a multi-objective optimisation problem can be enormous or infinitely many, it is desirable to provide the decision-maker with only a small, representative portion of all the nondominated solutions in the archive, thus entailing a truncation operation. Then, an important issue is when to truncate the archive. This can be done once a new solution generated, a batch of new solutions generated, or even using an unbounded archive to keep all nondominated solutions generated and truncate it later. Intuitively, the last approach may lead to a better result since we have all the information in hand before performing the truncation. In this paper, we study this issue and investigate the effect of the timing of truncating the archive. We apply well-established truncation criteria that are commonly used in the population maintenance procedure of MOEAs (e.g., crowding distance, hypervolume indicator, and decomposition). We show that, interestingly, truncating the archive once a new solution generated tends to be the best, whereas considering an unbounded archive is often the worst. We analyse and discuss this phenomenon. Our results highlight the importance of developing effective subset selection techniques (rather than employing the population maintenance methods in MOEAs) when using a large archive.

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