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A multi-strategy enhanced RIME-based metaheuristic with adaptive strategy collaboration for global optimization and
Xianmeng Zhao1, Yutong Duan1, Fan Liu2
1School of Innovation and Design, Wuhan Textile University, Wuhan, 430070, Hubei Province, China.
Scientific Reports
|May 18, 2026
Summary
The multi-strategy self-adaptive Rime Optimization (MSRIME) algorithm enhances complex landscape optimization. MSRIME improves search diversity and convergence stability for better accuracy and robustness in tasks like image segmentation.
Area of Science:
- Computational Intelligence
- Optimization Algorithms
- Metaheuristics
Background:
- Traditional Rime Optimization Algorithm (RIME) faces performance issues in complex optimization landscapes.
- Challenges include high dimensionality, strong multimodality, and demanding tasks like image segmentation.
Purpose of the Study:
- To develop a multi-strategy self-adaptive Rime Optimization (MSRIME) approach.
- Enhance search diversity and convergence stability for improved optimization performance.
Main Methods:
- Introduced a dynamically adjusted differential mutation factor for exploration-exploitation balance.
- Constructed a heterogeneous strategy pool with multiple update operators.
- Employed a probability-driven strategy selection scheme based on performance feedback.
Main Results:
- MSRIME demonstrated superior performance on CEC2017 and CEC2022 benchmark suites.
- Achieved best mean rankings in Friedman tests, outperforming state-of-the-art algorithms.
- Showcased effectiveness in multilevel threshold image segmentation with higher PSNR, SSIM, and FSIM values.
Conclusions:
- MSRIME offers an effective and robust optimization framework.
- Significant performance improvements achieved without increased computational complexity.
- Validated effectiveness in both benchmark testing and practical image segmentation applications.
