Integrating Soft Computing Techniques into the Assessment of Sustainable Technologies for Efficient Wave Energy Harvesting: A Review

Abstract

Wave energy is one of the most promising sources of renewable energy, offering vast potential for sustainable power generation. However, the development and deployment of wave energy systems face several challenges, including the selection of suitable technologies, optimal site identification, environmental concerns, and operational efficiency. In recent years, soft computing techniques including fuzzy logic, genetic algorithms, artificial neural networks, and hybrid intelligent systems have emerged as effective tools for handling the complexity, uncertainty, and non-linearity inherent in wave energy system assessment. This review aims to provide a comprehensive overview of how soft computing methods have been integrated into the evaluation and decision-making processes related to sustainable wave energy technologies. The paper systematically explores the role of these techniques in technology selection, performance prediction, environmental impact assessment, and site suitability analysis. Additionally, the review highlights the comparative strengths of various soft computing approaches in modeling real-world scenarios, where data may be imprecise or incomplete. Key findings indicate that soft computing not only enhances the robustness and adaptability of assessment frameworks but also facilitates multi-criteria decision-making and optimization in complex marine environments. Despite the growing body of research, gaps remain in standardizing frameworks and validating models across diverse geographic and environmental conditions. This review concludes by identifying current limitations and proposing future research directions aimed at advancing the application of soft computing in wave energy systems, ultimately contributing to the broader goal of sustainable and efficient marine energy harvesting.