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MCPSFOA:Multi-Strategy Enhanced Crested Porcupine-Starfish Optimization Algorithm for Global Optimization and Engineering Design 认领 引用
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作者 Hao Chen Tong Xu +2 位作者 Yutian Huang Dabo Xin Changting Zhong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期494-545,共52页
Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(... Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems. 展开更多
关键词 Global optimization starfish optimization algorithm crested porcupine optimizer metaheuristic Gaussian mutation population diversity enhancement
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A Comprehensive Framework for Nature-Inspired Photovoltaic Model Calibration and Explainable Surrogate-Based Sensitivity Analysis 认领 引用
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作者 Yan-Hao Huang Chung-Ming Kao 《Computers, Materials & Continua》 SCIE EI 2026年第6期2354-2379,共26页
Photovoltaic(PV)equivalent-circuit models are widely used for performance evaluation and diagnostics,but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape cur... Photovoltaic(PV)equivalent-circuit models are widely used for performance evaluation and diagnostics,but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape current-voltage(Ⅰ-Ⅴ)behavior.For nonlinear and strongly coupled PV models,conventional global sensitivity analysis can be computationally demanding and offer limited insight into effect direction and operating-point dependence.This study presents an method-oriented framework that integrates nature-inspired optimization with surrogate-based explainable global sensitivity analysis under a specified operating condition.The Starfish Optimization Algorithm(SFOA)is first used for parameter identification by searching for the optimal parameter set that minimizes the discrepancy between measured and model-predicted Ⅰ-Ⅴ data for the Single-Diode Model(SDM)and Double-Diode Model(DDM).A Random Forest(RF)surrogate is trained to approximate the mapping from voltage and parameters to output current.Its accuracy is evaluated on an independent test set,achieving RMSE/R2 of 0.001331/0.999366 for SDM and 0.003090/0.999394 for DDM.Sensitivity is quantified primarily using Shapley Additive Explanations(SHAP),with mean decrease in impurity(MDI)and one-factor-at-a-time(OFAT)analysis used for cross-validation.Under identical settings,SFOA achieves the best accuracy among competing optimizers,with best root-mean-square error(RMSE)values of 0.0008818 for SDM and 0.0008811 for DDM.The integrated SHAP,MDI,and OFAT analyses yield consistent importance structures,and the overall ranking for DDM follows the same trend as that for SDM.Within the examined±5%neighborhood around the calibrated optimum,the photocurrent is the dominant factor governing Ⅰ-Ⅴ behavior,whereas diode-branch parameters show secondary and condition-dependent effects,and resistive parameters mainly contribute fine-scale adjustments within the examined neighborhood.Overall,the proposed framework provides accurate calibration and interpretable global sensitivity insights that can support a practical workflow for model-based PV analysis under the considered condition. 展开更多
关键词 Photovoltaic parameter identification sensitivity analysis starfish optimization algorithm random forest nature-inspired algorithms
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