Browsing by Author "Bhuyan, Bikram Pratim"

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    A Comprehensive Survey of Manta Ray Foraging Optimization: Theory, Variants, Hybridization, and Applications
    (Springer Science and Business Media, 2025) Yahia, Selma; Taleb, Sylia Makhmoukh; Ait Saadi, Amylia; Meraihi, Yassine; Bhuyan, Bikram Pratim; Mirjalili, Seyedali; Ramdane-Cherif, Amar
    The Manta Ray Foraging Optimization (MRFO) algorithm is a recent Swarm-based meta-heuristic optimization algorithm inspired by the foraging behavior of manta rays in catching and hunting their prey, utilizing three main techniques (i.e.: chain foraging, somersault foraging, and cyclone foraging). Since its development by Zhao et al. (Neural Comput Appl 32:9777–9808, 2020; Eng Appl Artif Intell 87:103300, 2020), the MRFO algorithm has garnered significant attention among researchers and has been applied across various fields to solve real-world optimization problems. This is due to its simple structure, flexibility, ease of implementation, and reasonable convergence rate. This paper provides an extensive and in-depth survey of the MRFO algorithm including modification, multi-objective, and hybridized versions. It also examines the various applications of the MRFO algorithm in several domains of problems such as classification, feature selection, scheduling, robotics, photovoltaic power systems, optimal parameter control, and clustering. Furthermore, the results of the MRFO algorithm are compared with some well-regarded optimization meta-heuristics such as Differential Evolution (DE), Harmony Search (HS), Bat Algorithm (BA), Multi-Verse Optimizer (MVO), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Henry Gas Solubility Optimization (HGSO), and White Shark Optimizer (WSO). Finally, the paper proposes some potential future research directions to further advance the MRFO’s capability and applicability
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    A Monadic Second-Order Temporal Logic framework for hypergraphs
    (Springer Nature, 2024) Bhuyan, Bikram Pratim; Singh, Thipendra P.; Tomar, Ravi; Meraihi, Yassine; Ramdane-Cherif, Amar
    This study introduces a novel computational framework integrating monadic second-order temporal logic (MSOTL) with hypergraph models to enhance the predictive analysis and prediction of complex systems, with a specific focus on urban agriculture. Traditional graph-based models often fail to capture the intricate, high-order temporal dynamics inherent in such systems. By leveraging the expressive power of MSOTL within a hypergraph context, our approach enables a more nuanced representation of temporal and relational data, leading to improved predictive accuracy and deeper analytical insights. The framework was applied to a comprehensive dataset of urban agricultural practices, incorporating data from diverse farming sites across multiple countries. Our results demonstrate the model’s capability to outperform existing methods in predicting agricultural outcomes by effectively capturing both the spatial and temporal complexities of urban farming data. The study not only advances the theoretical understanding of hypergraph-based temporal logic modeling but also offers an application for urban agricultural planning and management.