Optimizing Solar Photovoltaic Panels: A Fuzzy Logic-Based MCDM Approach Using DEMATEL and a Bio-Inspired Cheetah Algorithm

Introduction:This study aims to optimize solar photovoltaic (PV) panel performance by integrating the bio-inspired Cheetah algorithm with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The proposed approach evaluates key factors affecting PV efficiency and provides a decision-making framework for enhancing solar energy technology. Methodology:A Multi-Criteria Decision-Making (MCDM) approach was employed, utilizing DEMATEL to identify and rank 13 crucial criteria affecting PV panel performance. A direct-relation matrix was constructed, normalized, and analyzed to determine cause-effect relationships. The Cheetah algorithm was incorporated to refine the optimization process. Findings:The results indicate that energy conversion efficiency, thermal management, and shading tolerance are the most influential factors. Material cost, lifecycle performance, and system reliability also play pivotal roles in PV technology adoption. The study highlights the importance of adaptive technologies, scalability, and environmental considerations in solar panel optimization. Research Implications: The findings provide valuable insights for researchers and engineers in designing more efficient and sustainable PV systems. The DEMATEL-based prioritization framework facilitates structured decision-making, helping stakeholders improve solar energy solutions. Practical Implications: By addressing the identified key factors, manufacturers can enhance PV panel performance, reduce costs, and promote widespread adoption of solar technology. The integration of bio-inspired optimization techniques enables better adaptability to environmental variations. Originality/Value:This research introduces a novel combination of the DEMATEL method with a bio-inspired Cheetah optimization algorithm, offering a robust framework for PV panel evaluation and performance enhancement. The study contributes to the advancement of sustainable energy technologies.