ArticleBiomimetics (Basel, Switzerland)2023
I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- MHO: A Modified Hippopotamus Optimization Algorithm for Global Optimization and Engineering Design Problems.Biomimetics (Basel, Switzerland) · 2025Article
- A hybrid Prairie INFO fission naked algorithm with stagnation mechanism for the parametric estimation of solar photovoltaic systems.Scientific reports · 2025Article
- Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators.Biomimetics (Basel, Switzerland) · 2024Article
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- Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative study.Scientific reports · 2024Article
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2 authors.
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Abstract
The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules.
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