A Parallel Multi-Verse Optimizer for Application in Multilevel Image Segmentation

Xiaopeng Wang, Jeng-Shyang Pan, Shu-Chuan Chu

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)
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Multi-version optimizer (MVO) inspired by the multi-verse theory is a new optimization algorithm for challenging multiple parameter optimization problems in the real world. In this paper, a novel parallel multi-verse optimizer (PMVO) with the communication strategy is proposed. The parallel mechanism is implemented to randomly divide the initial solutions into several groups, and share the information of different groups after each fixed iteration. This can significantly promote the cooperation individual of MVO algorithm, and reduce the deficiencies that the original MVO is premature convergence, search stagnation and easily trap into local optimal search space. To confirm the performance of the proposed scheme, the PMVO algorithm was compared with the other well-known optimization algorithms, such as gray wolf optimizer (GWO), particle swarm optimization (PSO), multi-version optimizer (MVO), and parallel particle swarm optimization (PPSO) under CEC2013 test suite. The experimental results prove that the PMVO is superior to the other compared algorithms. In addition, PMVO is also applied to solve complex multilevel image segmentation problems based on minimum cross entropy thresholding. The application results appear that the proposed PMVO algorithm can achieve higher quality image segmentation compared to other similar algorithms.

Original languageEnglish
Article number8995472
Pages (from-to)32018 - 32030
Number of pages13
JournalIEEE Access
Publication statusPublished - 24 Feb 2020
Externally publishedYes

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/


  • Meta-heuristic optimization
  • parallel multi-verse optimizer
  • multilevel image segmentation
  • minimum cross entropy thresholding


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