Academic Journal
Enhanced Moss Growth Optimization with Benchmark Validation and a Wastewater Treatment Prediction Case Study.
| Title: | Enhanced Moss Growth Optimization with Benchmark Validation and a Wastewater Treatment Prediction Case Study. |
|---|---|
| Authors: | Li, Zongkun, Tang, Shanfa |
| Source: | Biomimetics (2313-7673); Sep2026, Vol. 11 Issue 9, p628, 19p |
| Subject Terms: | Metaheuristic algorithms, Wastewater treatment, Support vector machines, Optimization algorithms, Shapley Additive Explanations, Mathematical optimization, Benchmark testing (Engineering) |
| Abstract: | Complex optimization tasks in data-driven prediction and engineering applications often involve nonlinear, multimodal, and ill-conditioned objective functions. This study proposes an Enhanced Moss Growth Optimization algorithm (EMGO), an improved variant of the baseline MGO framework, to enhance exploratory step-size control and local covariance exploitation. EMGO incorporates two key algorithmic augmentations: a budget-adaptive jump regulation mechanism that balances global dispersal and fine-grained refinement, and a shrinkage-regularized covariance-guided sampling operator with relative eigenvalue flooring to exploit correlation structures among elite individuals without rank deficiency. The proposed algorithm is evaluated on the CEC2017 benchmark suite across 50 and 100 dimensions with 29 test functions, 30 independent runs, and a budget of 3 × 10 5 function evaluations per run, compared against ten state-of-the-art optimizers including CMA-ES, L-SHADE, SBO, and baseline MGO. Nonparametric Friedman ranking, Holm-adjusted Wilcoxon signed-rank tests, and runtime-matched analyses demonstrate that EMGO achieves highly competitive performance across high-dimensional landscapes. Furthermore, EMGO is applied to tune support vector regression (SVR) hyperparameters for effluent suspended solid (SS) prediction using the UCI Water Treatment Plant dataset under an expanding-window rolling-origin cross-validation scheme. EMGO-SVR achieves superior predictive accuracy ( RMSE = 5.58 ± 0.64 , MAE = 3.97 ± 0.46 , R 2 = 0.889 ± 0.028 ), outperforming standard SVR, tree-based ensembles, and Bayesian optimization baselines. SHAP-based feature importance analysis confirms the physical and process consistency of the model predictions. [ABSTRACT FROM AUTHOR] |
| Copyright of Biomimetics (2313-7673) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhanced Moss Growth Optimization with Benchmark Validation and a Wastewater Treatment Prediction Case Study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Zongkun%22">Li, Zongkun</searchLink><br /><searchLink fieldCode="AR" term="%22Tang%2C+Shanfa%22">Tang, Shanfa</searchLink> – Name: TitleSource Label: Source Group: Src Data: Biomimetics (2313-7673); Sep2026, Vol. 11 Issue 9, p628, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Wastewater+treatment%22">Wastewater treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+testing+%28Engineering%29%22">Benchmark testing (Engineering)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Complex optimization tasks in data-driven prediction and engineering applications often involve nonlinear, multimodal, and ill-conditioned objective functions. This study proposes an Enhanced Moss Growth Optimization algorithm (EMGO), an improved variant of the baseline MGO framework, to enhance exploratory step-size control and local covariance exploitation. EMGO incorporates two key algorithmic augmentations: a budget-adaptive jump regulation mechanism that balances global dispersal and fine-grained refinement, and a shrinkage-regularized covariance-guided sampling operator with relative eigenvalue flooring to exploit correlation structures among elite individuals without rank deficiency. The proposed algorithm is evaluated on the CEC2017 benchmark suite across 50 and 100 dimensions with 29 test functions, 30 independent runs, and a budget of 3 × 10 5 function evaluations per run, compared against ten state-of-the-art optimizers including CMA-ES, L-SHADE, SBO, and baseline MGO. Nonparametric Friedman ranking, Holm-adjusted Wilcoxon signed-rank tests, and runtime-matched analyses demonstrate that EMGO achieves highly competitive performance across high-dimensional landscapes. Furthermore, EMGO is applied to tune support vector regression (SVR) hyperparameters for effluent suspended solid (SS) prediction using the UCI Water Treatment Plant dataset under an expanding-window rolling-origin cross-validation scheme. EMGO-SVR achieves superior predictive accuracy ( RMSE = 5.58 ± 0.64 , MAE = 3.97 ± 0.46 , R 2 = 0.889 ± 0.028 ), outperforming standard SVR, tree-based ensembles, and Bayesian optimization baselines. SHAP-based feature importance analysis confirms the physical and process consistency of the model predictions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Biomimetics (2313-7673) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/biomimetics11090628 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 628 Subjects: – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Wastewater treatment Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Shapley Additive Explanations Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Benchmark testing (Engineering) Type: general Titles: – TitleFull: Enhanced Moss Growth Optimization with Benchmark Validation and a Wastewater Treatment Prediction Case Study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Zongkun – PersonEntity: Name: NameFull: Tang, Shanfa IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23137673 Numbering: – Type: volume Value: 11 – Type: issue Value: 9 Titles: – TitleFull: Biomimetics (2313-7673) Type: main |
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