{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:23:15Z","timestamp":1788456195535,"version":"build-2803163510"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>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\u00d7105 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\u00b10.64, MAE=3.97\u00b10.46, R2=0.889\u00b10.028), 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.<\/jats:p>","DOI":"10.3390\/biomimetics11090628","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T16:21:21Z","timestamp":1788452481000},"page":"628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Moss Growth Optimization with Benchmark Validation and a Wastewater Treatment Prediction Case Study"],"prefix":"10.3390","volume":"11","author":[{"given":"Zongkun","family":"Li","sequence":"first","affiliation":[{"name":"School of Petroleum Engineering, Yangtze University, Wuhan 430100, China"},{"name":"Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering, Yangtze University, Wuhan 430100, China"},{"name":"National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China"},{"name":"State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Shanfa","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Petroleum Engineering, Yangtze University, Wuhan 430100, China"},{"name":"Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering, Yangtze University, Wuhan 430100, China"},{"name":"National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China"},{"name":"State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","unstructured":"Nocedal, J., and Wright, S.J. 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