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<p>Traditional Rime Optimization Algorithm (RIME) often encounters performance degradation when dealing with complex optimization landscapes, such as high dimensionality, strong multimodality, and application-driven image segmentation tasks. To overcome these limitations, this study develops a multi-strategy self-adaptive Rime Optimization approach, termed MSRIME, which enhances both search diversity and convergence stability through adaptive and collaborative mechanisms. Specifically, MSRIME introduces a dynamically adjusted differential mutation factor to regulate exploration and exploitation along the optimization process. In addition, a heterogeneous strategy pool composed of multiple update operators is constructed, enabling complementary search behaviors at different optimization stages. A probability-driven strategy selection scheme based on performance feedback is further employed to adaptively allocate search resources among strategies. The proposed algorithm is evaluated on the CEC2017 and CEC2022 benchmark suites and compared with several state-of-the-art metaheuristic algorithms. Experimental results show that MSRIME consistently achieves superior performance in terms of optimization accuracy and robustness. In the Friedman statistical test, MSRIME obtains the best mean rankings of 1.93 and 1.77 on CEC2017 (30D and 100D), and 1.67 and 2.17 on CEC2022 (10D and 20D), respectively, outperforming all competing algorithms. Furthermore, MSRIME is applied to multilevel threshold image segmentation using Otsu’s criterion. The results demonstrate that the proposed algorithm achieves higher PSNR, SSIM, and FSIM values across different images and threshold levels, indicating its effectiveness in practical applications. Overall, the proposed MSRIME provides an effective and robust optimization framework, achieving significant performance improvements without increasing computational complexity.</p>

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A multi-strategy enhanced RIME-based metaheuristic with adaptive strategy collaboration for global optimization and art image segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月18日

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  • Xianmeng Zhao,
  • Yutong Duan,
  • Fan Liu

摘要

Traditional Rime Optimization Algorithm (RIME) often encounters performance degradation when dealing with complex optimization landscapes, such as high dimensionality, strong multimodality, and application-driven image segmentation tasks. To overcome these limitations, this study develops a multi-strategy self-adaptive Rime Optimization approach, termed MSRIME, which enhances both search diversity and convergence stability through adaptive and collaborative mechanisms. Specifically, MSRIME introduces a dynamically adjusted differential mutation factor to regulate exploration and exploitation along the optimization process. In addition, a heterogeneous strategy pool composed of multiple update operators is constructed, enabling complementary search behaviors at different optimization stages. A probability-driven strategy selection scheme based on performance feedback is further employed to adaptively allocate search resources among strategies. The proposed algorithm is evaluated on the CEC2017 and CEC2022 benchmark suites and compared with several state-of-the-art metaheuristic algorithms. Experimental results show that MSRIME consistently achieves superior performance in terms of optimization accuracy and robustness. In the Friedman statistical test, MSRIME obtains the best mean rankings of 1.93 and 1.77 on CEC2017 (30D and 100D), and 1.67 and 2.17 on CEC2022 (10D and 20D), respectively, outperforming all competing algorithms. Furthermore, MSRIME is applied to multilevel threshold image segmentation using Otsu’s criterion. The results demonstrate that the proposed algorithm achieves higher PSNR, SSIM, and FSIM values across different images and threshold levels, indicating its effectiveness in practical applications. Overall, the proposed MSRIME provides an effective and robust optimization framework, achieving significant performance improvements without increasing computational complexity.