Improved snow ablation optimization for multilevel threshold image segmentation

Rui Zhong, Chao Zhang, Jun Yu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Snow ablation optimization (SAO) is a novel metaheuristic algorithm (MA). However, we observed certain issues in the original SAO, such as poor capacity in escaping from local optima and slow convergence. To address these limitations, we introduce two strategies: the asynchronous update strategy (AUS) and the top-k survival mechanism. We name our proposal SAOk-AUS. In the original SAO, the segregation of search and update delays the improved information sharing, and AUS integrates update processes following each individual’s search behavior, facilitating superior knowledge from elites. Additionally, the original SAO adopts an all-acceptance selection principle, maintaining diversity but cannot guarantee the solution quality. Thus, we introduce the top-k survival mechanism to ensure the survival of elites. Comprehensive numerical experiments on CEC2013 and CEC2020 benchmark functions, engineering problems, and image segmentation tasks were conducted to evaluate our proposal against eight state-of-the-art MAs. The experimental results and statistical analyses confirm the efficiency of SAOk-AUS. Moreover, the ablation experiments investigate the contribution of two strategies, and we recommend using both proposed strategies simultaneously. The source code of this research is made available in https://github.com/RuiZhong961230/SAO_k-AUS.

Original languageEnglish
Article number16
JournalCluster Computing
Volume28
Issue number1
DOIs
StatePublished - 2025/02

Keywords

  • Asynchronous update strategy (AUS)
  • Image segmentation
  • Snow ablation optimization (SAO)
  • Top-k survival mechanism

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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