Aureum Academic Journal (AAJ) is a quarterly academic journal published by the Aureum Academic Press, AAJ publishes original researches in all fields of Technology (Computer Science, Computer Engineering, Information Technology, Artificial Intelligent, Security, computer Graphics and Data Science ). AAJ is a Peer-Reviewed Open Access journal. AAJ provides immediate, worldwide, barrier-free access to the full text of research articles without requiring a subscription to the journal.
Toward Autonomous 6G Networks: A Trust-Centric and Privacy-Preserving Framework
ABSTRACT: The move towards sixth-generation networks poses unprecedented challenges in terms of security, trust, and privacy due to the integration of artificial intelligence, Internet of Things, and global communication networks. In this context, this paper aims to provide an exhaustive systematic literature review of existing 6G research with existing trust models proposed by the International Telecommunication Union (ITU-T) and analyze the interrelated relationship between privacy preservation, energy efficiency, and autonomous systems. This research is based on an exhaustive analysis of existing peer-reviewed articles from 2020 to 2026, white papers, and existing frameworks. It is evident from this analysis that existing research is largely focused on the individual optimization of privacy preservation, energy efficiency, and autonomous systems, leading to an inherent trade-off. This paper aims to provide an exhaustive framework for integrating privacy preservation, energy efficiency, and autonomous systems as pillars of 6G networks. Key findings include that Edge Intelligence, Federated Learning, Zero Trust Architecture, and Post-Quantum Cryptography act as key enablers in meeting these often conflicting requirements. The study concludes by identifying 12 distinct research gaps and suggesting future directions toward cooperative, AI-driven, and quantum-resistant trust architectures for 6G. This study contributes to the SAMRNET (Security, Architecture, Management, and Resilience of NETworks) research agenda by establishing a comprehensive foundation for trust-centric 6G network design.
Copyright (c) 2026 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence–Augmented Statistical Inference: Integrating Machine Learning with Classical Estimation and Hypothesis Testing
The convergence of machine learning and classical statistical inference has given rise to a new paradigm in data analysis, enabling researchers to address high-dimensional, complex, and non-linear data structures that challenge traditional inferential techniques. While classical methods such as maximum likelihood estimation and hypothesis testing provide strong theoretical guarantees and interpretability, they often struggle in modern data-rich environments. Conversely, machine learning models offer remarkable predictive power but frequently lack transparency and rigorous uncertainty quantification. This study examines artificial intelligence–augmented statistical inference as a principled integration of these two approaches, aiming to preserve statistical rigor while enhancing flexibility and performance. By synthesizing theoretical foundations and empirical evidence across diverse application domains—including neuroimaging, model-based deep learning systems, and survival analysis—the paper demonstrates how hybrid frameworks can improve estimation accuracy, hypothesis testing robustness, and interpretability. The analysis highlights the role of model-based deep learning, penalized estimation, and machine learning–assisted feature selection in maintaining inferential validity and uncertainty quantification. Overall, this work establishes AI-augmented statistical inference as a robust and interpretable framework for advancing modern data-driven science while retaining the foundational principles of classical statistics.
Copyright (c) 2025 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Securing the Quantum Era: A Comprehensive Review of Post-Quantum Cryptography, Threat Models, Algorithmic Foundations, and Standardization Pathways
The advent of fault-tolerant quantum computing precipitates a foundational threat to the security of global digital infrastructure by rendering obsolete the mathematical assumptions underlying classical public-key cryptography. Widely deployed algorithms, including RSA, ECDSA, and Diffie-Hellman, which rely on the computational intractability of integer factorization and discrete logarithm problems, are vulnerable to polynomial-time attacks via Shor's algorithm. Concurrently, Grover's algorithm imposes a quadratic reduction in the security strength of symmetric primitives. In response, Post-Quantum Cryptography (PQC) has emerged as a critical field of research, dedicated to developing cryptographic systems secure against both classical and quantum attacks, while remaining deployable on existing classical hardware. This paper presents a comprehensive and in-depth examination of PQC, analyzing the five principal families: lattice-based, code-based, multivariate, hash-based, and isogeny-based cryptography. Each family is scrutinized through rigorous mathematical exposition, conceptual analysis, comparative performance evaluations, and contemporary security assessments. The study situates PQC within the evolving global threat landscape, provides a detailed analysis of the National Institute of Standards and Technology (NIST) PQC standardization process, and addresses critical implementation challenges such as constrained environments, migration strategies, hybrid cryptographic modes, and the imperative for cryptographic agility. The paper concludes by delineating essential future research directions vital for constructing a robust, quantum-resilient global cryptographic infrastructure.
Design and Implementation of a Durable and Secure Enterprise Service Bus Framework for Modern Web Applications
This project presents the design, implementation, and evaluation of a secure, durable, and service-oriented web application built on an Enterprise Service Bus (ESB) architecture. Motivated by the growing need for scalable, modular, and security-hardened online systems, the study integrates principles of Service-Oriented Architecture (SOA) with UMLsec-driven secure design and Java EE’s Role-Based Access Control (RBAC). The ESB serves as the integration backbone, enabling seamless communication across heterogeneous services while supporting dynamic orchestration and protocol interoperability. The project employs a hybrid lifecycle model combining agile and plan-driven approaches to accommodate evolving requirements without compromising system discipline. Durability and quality-in-use characteristics—evaluated using multi-criteria decision-making frameworks—demonstrate high trustworthiness, maintainability, usability, and long-term operational resilience. Comprehensive testing, including security, performance, usability, and durability assessments, confirms that the system meets critical functional and non-functional requirements. The results validate that an ESB-based SOA architecture, enhanced with secure development methodologies and user-centric design, provides a robust foundation for enterprise-level web applications requiring reliability, extensibility, and sustained user satisfaction
Developing an Intelligent Tutoring System for Personalized Skill Development Using Reinforcement Learning
The one-size-fits-all model of traditional education is increasingly inadequate for addressing diverse learner needs. Intelligent Tutoring Systems (ITS) offer a solution but are often limited by static, hand-crafted pedagogical rules that cannot optimize long-term learning trajectories. This paper presents the design, implementation, and empirical validation of RL-Tutor, a novel ITS that leverages Deep Reinforcement Learning (RL) to provide dynamic, personalized instruction. RL-Tutor integrates a Deep Knowledge Tracing model based on a Dynamic Key-Value Memory Network (DKVMN) to maintain a rich, continuous representation of the student's knowledge state. This state serves as the input to a Proximal Policy Optimization (PPO) agent, which functions as the pedagogical module, selecting optimal actions from a hierarchical space including problem selection, hint provision, and instructional review. A critical contribution is the formulation of a multi-faceted reward function that balances immediate performance, learning efficiency, and long-term knowledge retention. Due to the sample inefficiency of RL, the agent was first trained in a high-fidelity simulated environment with a population of 10,000 synthetic students. The trained system was then evaluated against a rule-based tutor and a static tutor in a between-subjects human study (N=90) in the domain of introductory Python programming. Results show that RL-Tutor led to significantly higher normalized learning gains (0.72 vs. 0.58 for rule-based and 0.45 for static, p < 0.01) and better retention in a one-week delayed post-test. Analysis of the learned policy revealed emergent, pedagogically sound strategies such as adaptive hinting and implicit spaced repetition. This work establishes that RL can autonomously discover complex, effective teaching policies that are tailored to individual learners and outperform traditional ITS architectures.
Portable Few-Shot Learning for Early Warning Systems in Small Private Online Courses: A CNN-Based Predictive Framework for Student Performance
This study develops a portable early warning system designed to predict student academic performance in Small Private Online Courses (SPOCs) by leveraging few-shot learning techniques and convolutional neural networks (CNNs). Addressing the persistent challenges posed by limited sample sizes and the absence of face-to-face interactions in asynchronous online environments, the research explores whether small-sample behavioral data derived from multiple SPOCs can support reliable and transferable predictive models. The dataset comprises more than 4.4 million LMS log entries collected from four online courses sharing similar instructional designs and taught by a single instructor. After comprehensive preprocessing—encompassing feature extraction, weekly aggregation, and normalization—18 week-specific CNN models were trained to capture the temporal progression of student learning behaviors. The results indicate that meaningful prediction accuracy emerges by the fifth week, with performance exceeding 80% from week eight onward. Portability was further validated by applying the model to an additional course, where accuracy remained at or above 81%, confirming its robustness under consistent instructional conditions. The findings highlight the potential of few-shot learning to sustain predictive performance despite limited training samples, offering educators a viable foundation for timely interventions and institutional adoption of precision-driven academic support systems.
Copyright (c) 2024 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Adaptive Deep Learning Architectures for Real-Time Data Streams in Edge Computing Environments
The increasing reliance on real-time analytics within edge computing environments has underscored the need for adaptive deep learning architectures capable of handling continuous data streams under limited computational and energy resources. Unlike traditional cloud-based frameworks, edge computing shifts computation closer to the data source, minimizing latency, preserving data privacy, and supporting responsive decision-making in dynamic contexts. This paper comprehensively examines adaptive deep learning models that autonomously adjust their structure and parameters in response to evolving data distributions and resource constraints. The study explores several key methodologies—such as master–surrogate deep neural networks, context–adaptive DNN atom partitioning, distributed inference with fused layer partitioning, and reinforcement learning–driven resource scheduling. Comparative analyses demonstrate that these adaptive frameworks achieve substantial performance gains, including up to 23.31% accuracy improvement, 62.14% latency reduction, and over 50% energy savings across diverse edge devices. Furthermore, the paper evaluates real-world deployments in smart cities, healthcare monitoring, Industry 4.0 automation, autonomous vehicles, and environmental sensing, illustrating the scalability and robustness of adaptive architectures in heterogeneous edge ecosystems. The discussion concludes by outlining current challenges—such as hardware diversity, continual learning, and energy constraints—and highlights future research directions including federated adaptation, neuromorphic hardware integration, and standardized benchmarking for edge AI.
Blockchain-Driven Security Models for Privacy Preservation in IoT-Based Smart Cities
The convergence of blockchain technology and the Internet of Things (IoT) has emerged as a transformative paradigm for developing secure, resilient, and privacy-preserving infrastructures in smart cities. Despite the rapid adoption of IoT across urban environments, centralized data management models continue to expose significant vulnerabilities, including single points of failure and data privacy breaches. This study proposes a blockchain-driven security framework that addresses these issues through decentralized identity verification, privacy-preserving data sharing, and scalability-enhancing mechanisms. The model integrates a Proof-of-Internet-of-Things (PIoT) consensus protocol with local peer networks and double-blind consent-driven data sharing to achieve both security and efficiency. Through simulation-based evaluations and real-world case studies in healthcare, traffic, and energy management, the proposed architecture demonstrates superior performance in transaction throughput, latency reduction, and energy efficiency compared with traditional blockchain systems. The results indicate that blockchain can substantially enhance data integrity and trust while maintaining privacy compliance under urban-scale IoT deployments. This research contributes to the growing body of knowledge by bridging the gap between theoretical blockchain constructs and practical, resource-constrained IoT applications, offering a scalable blueprint for secure smart city ecosystems.
Copyright (c) 2023 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Metaheuristics in Sustainable and Green Optimization
The accelerating global pursuit of sustainability has placed optimization at the forefront of achieving environmental, economic, and social balance. This study presents a comprehensive review of metaheuristic algorithms as powerful computational tools for addressing sustainable and green optimization challenges. By examining a broad range of classical and modern metaheuristics—including bio-inspired, physics-based, swarm intelligence, and hybrid models—this work explores how these algorithms are utilized to minimize energy consumption, carbon emissions, and resource waste across key sectors such as renewable energy systems, smart grids, sustainable manufacturing, and green logistics. The paper emphasizes the role of hybrid and intelligent adaptive metaheuristics in enhancing convergence speed, robustness, and scalability in complex, multi-objective optimization scenarios. Comparative analyses reveal the superiority of hybrid models in achieving accurate, energy-efficient, and environmentally responsible outcomes. Furthermore, the study highlights persistent challenges related to computational cost, parameter sensitivity, and real-time adaptability. By consolidating current findings and identifying open research directions—such as self-adaptive learning-based frameworks, unified benchmarking standards, and quantum-inspired metaheuristics—this review underscores the transformative potential of metaheuristic optimization in advancing the global sustainability agenda.
Systematic Review of Regression Algorithms for Predictive Analytics
This systematic review provides an in-depth examination of regression algorithms applied in predictive analytics across multiple domains, including engineering, transportation, and education. The study synthesizes developments in both traditional statistical and modern machine learning (ML) regression models, outlining their evolution, taxonomy, and comparative performance. Traditional multiple linear regression (MLR) has been foundational for predictive analysis but struggles with scalability and computational efficiency in the era of big data. To address these limitations, adaptations such as MapReduce-based MLR have been proposed, enabling distributed computation for large-scale data processing. Simultaneously, hybrid and ML-based regression models—such as support vector regression (SVR), regression trees (CART), and integrated linear–deep learning frameworks—demonstrate superior performance in modeling complex, non-linear relationships. This review establishes a structured taxonomy differentiating regression approaches based on assumptions, computational properties, and domain-specific requirements, highlighting the balance between model interpretability and predictive power. Comparative evaluations using metrics such as variance explained (R²), mean squared error (MSE), and computational scalability reveal that hybrid regression models often outperform conventional techniques in accuracy and adaptability. The review also identifies persistent challenges related to big data management, model interpretability, data harmonization, and real-time scalability. Emerging research trends emphasize explainable AI (XAI), ensemble regression strategies, and enhanced distributed computing frameworks to overcome these obstacles. Ultimately, this study contributes a holistic understanding of regression-based predictive analytics, guiding researchers and practitioners toward developing scalable, interpretable, and domain-adaptive models for next-generation data-driven decision-making.
Copyright (c) 2022 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
An Experimental Assessment of AI-Based Legal Decision-Making Systems in Contract Analysis and Risk Detection
This comprehensive experimental study evaluates the performance, reliability, and practical applicability of AI-based legal decision-making systems in contract analysis and risk detection. Utilizing a corpus of 5,247 contracts with expert-validated annotations from 12 legal professionals, we benchmark four classes of AI systems—rule-based, supervised machine learning (XGBoost), fine-tuned transformer models (Legal-BERT), and large language models (GPT-4, Claude 3)—across multiple dimensions critical to legal practice. The Key Findings of this research : a) Performance Variability: Fine-tuned Legal-BERT achieved the highest overall clause classification F1-score (0.923, 95% CI [0.917, 0.929]), but exhibited significant degradation in cross-jurisdictional applications (28.4% performance drop from US to UK contracts). b) Risk Detection Gaps: All systems demonstrated decreasing recall with increasing risk severity. GPT-4 missed 18.2% of high-severity risks (severity ≥4), while Legal-BERT missed 12.3% of total risk severity weight (FNRP metric). c)Decision Inconsistency: LLMs showed substantial inconsistency, with GPT-4 achieving only 0.81 intra-model Jaccard similarity across identical inputs and 14.7% decision variation on identical clause phrasings. d)Domain-Specific Performance: Rule-based systems performed adequately on standardized agreements (NDA: F1=0.812) but failed catastrophically on complex contracts (M&A: F1=0.432). e) Cost-Effectiveness: Local fine-tuned models provided 92.3% of GPT-4's performance at 3.5% of the cost ($0.0087 vs $0.2478 per document).We introduce two novel legal-specific metrics—False-Negative Risk Penalty (FNRP) and Severity-Weighted F1 (SwF1)—that better capture the asymmetric cost structure of legal errors. Based on our empirical findings, we propose a three-tier human-in-the-loop deployment framework that reduces attorney review time by 64% while maintaining 99.7% risk coverage. The study establishes evidence-based performance thresholds for safe deployment, recommending against autonomous use of any system with FNRP > 0.15 or cross-jurisdiction performance degradation > 25%. Our findings challenge optimistic claims of AI autonomy in legal decision-making and provide a rigorous, reproducible framework for evaluating legal AI systems in practice.
Computer Forensics: Evolution and Its Pivotal Role in Modern Investigations
The preservation, identification, retrieval, documentation, and evaluation of laptop facts are the honest primary goals of computer forensics. Many computer forensics guidelines and approaches that want to be described and established are tested in this have a look at. Data have to be undamaged and available for evaluation. Authenticity of the information is likewise guaranteed. As a end result, pc forensic specialists extract and take a look at facts from storage devices used on the scene of a digital crime the usage of contemporary strategies and device. Examining some of these forensic technologies is the number one objective of this take a look at. The aim of the cutting-edge take a look at is to evaluate and comparison the key variations and similarities across the extraordinary forensic equipment as a way to determine which characteristics need to be progressed for effective garage device autopsy.
Comprehensive Review and Hybrid Evolution of Teaching–Learning-Based Optimization
Teaching–Learning-Based Optimization (TLBO) stands out as a novel population‐based metaheuristic inspired by the pedagogical process in a classroom, in which a teacher imparts knowledge to learners and the learners enhance their performance by mutual interaction. This paper provides a comprehensive review of TLBO along with its extensive hybrid and intelligent extensions developed over recent years. We examine the fundamental algorithmic principles of TLBO—its parameter‐free nature, two-phase (teacher and learner) approach, and inherent simplicity—and contrast its performance across a range of benchmarks and real-world engineering optimization problems. In addition, we survey various taxonomic categories such as adaptive TLBO, multi-objective TLBO, discrete variants, and hybrids that integrate TLBO with other metaheuristics including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Robust Tabu Search (RTS), and Harmony Search (HS)6. Special attention is given to recent innovations in hybrid frameworks, such as the TLBO–RTS and TLBO–CO algorithms, where complementary search techniques enhance global exploration while preserving the rapid convergence property. Finally, the paper discusses theoretical aspects including convergence properties, computational complexity, and outlines current challenges and promising directions for future research.
5G-Enabled Internet of Things: Latency Optimization through AI-Assisted Network Slicing
The confluence of Fifth-Generation (5G) wireless technology and the Internet of Things (IoT) heralds a new era of hyper-connectivity, enabling transformative applications from autonomous vehicles and industrial automation to extended reality and remote surgery. A critical performance indicator for many of these applications is ultra-reliable low-latency communication (URLLC), where delays must be bounded within milliseconds. However, the heterogeneous and dynamic nature of IoT traffic presents a monumental challenge to consistently meeting these stringent latency requirements. Traditional network management paradigms, which are largely static and reactive, are ill-suited for this task.This paper posits that the synergy of two cornerstone 5G technologies—Network Slicing and Artificial Intelligence (AI)—provides the foundational architecture and intelligent control mechanism necessary to achieve dynamic latency optimization at scale. Network Slicing allows for the creation of multiple logical, end-to-end virtual networks on a shared physical infrastructure, each tailored to specific service requirements. Meanwhile, AI and Machine Learning (ML) offer the predictive and adaptive capabilities to manage these slices proactively.This comprehensive review and analytical paper delves into the architecture of 5G-standalone (SA) systems to elucidate the enablers of low latency. It then provides a detailed exposition of network slicing as a resource isolation mechanism. The core of the paper is a thorough investigation into how various AI/ML paradigms—including supervised learning, reinforcement learning, and deep learning—can be integrated into the network control loop to predict traffic surges, dynamically allocate resources, and proactively reconfigure slices. We present a conceptual framework for an AI-assisted Network Slicing orchestration system, detailing its functional components and operational workflow. Furthermore, we analyze the significant challenges impeding widespread deployment, such as data collection, model training, security, and standardization. Through this analysis, we demonstrate that AI-assisted network slicing is not merely an enhancement but a critical imperative for realizing the full potential of latency-critical 5G-IoT ecosystems.
Automated Software Testing through Large Language Models: Opportunities and Challenges
The rapid evolution of software systems and the increasing complexity of modern applications have driven the need for innovative testing methodologies. Traditional techniques, often burdened by manual intervention and limited scalability, are increasingly challenged by the demands for speed, flexibility, and comprehensive quality assurance. Large Language Models (LLMs) offer a promising avenue for transforming automated software testing. This research examines the opportunities and challenges of integrating LLMs into testing workflows, supported by extensive literature, empirical surveys, and real-world case studies.Key opportunities include automated test-case generation, program repair, test-oracle creation, debugging assistance, and the development of self-healing testing systems. For instance, surveys show that approximately 51% of studies focus on test-case generation, while 26% address program repair, 11% focus on test-oracle creation, and 12% on debugging assistance. Concurrently, several challenges hinder the seamless adoption of LLM-based testing. These include the risk of hallucinated outputs, context window limitations, security and privacy issues, high computational costs, integration complexities, and potential biases in generated tests.This paper comprehensively reviews the theoretical underpinnings and practical implementations of LLM-driven test automation while proposing pathways for future research and development. Ultimately, LLMs are positioned as transformative tools whose effective integration could greatly enhance testing efficiency and reliability. However, careful design and human oversight remain critical to overcome the inherent challenges.
Hybrid Big Data Analytics: Integrating Structured and Unstructured Data for Predictive Intelligence
Hybrid big data analytics has emerged as a compelling paradigm for predictive intelligence, yet most operational pipelines still privilege a single modality—either structured relational data or unstructured text—thereby under-exploiting complementary signals. This paper proposes a unified framework that integrates structured records (e.g., time-series sensors, tabular attributes) with unstructured corpora (e.g., clinical narratives, web-scale text) through a multi-modal deep learning architecture coupled with scalable clustering and query optimization. The method fuses static encoders, temporal CNN/LSTM modules, and text representations (e.g., document embeddings with BiLSTM/CNN) in a learned fusion layer, and augments inference with a Gaussian Mixture Model optimized by a bio-inspired Salp Swarm Algorithm for low-latency, distributed querying. Experiments across two representative domains—infectious-disease forecasting and Industry 4.0 cycle-time projection—demonstrate consistent gains over single-modality baselines in AUROC, F1, MAE, and AUPRC, while preserving near real-time responsiveness on commodity GPU/CPU clusters. We discuss integration complexity, interpretability challenges, and deployment constraints, and delineate practical pathways for edge-side execution, transfer learning across domains, and explainability overlays. By systematically bridging structured and unstructured modalities, the study evidences material performance improvements and offers a robust template for multimodal analytics in high-stakes environments.
The Evolution of Metaheuristics: From Classical to Intelligent Hybrid Frameworks
Metaheuristic algorithms have experienced unprecedented evolution over the past several decades, emerging as potent stochastic optimization tools across a wide spectrum of real-world applications. This article provides a comprehensive review of the evolution of metaheuristics, tracing their origins from classical trajectory-based and population-based approaches to the modern era characterized by intelligent hybrid frameworks that integrate machine learning, reinforcement learning, and adaptive parameter tuning. In the early stages, metaheuristics were mainly inspired by natural phenomena—from the cooling process in simulated annealing to the collective behaviors observable in swarm intelligence—thereby establishing a robust foundation for solving complex global optimization problems23. More recently, over 500 metaheuristic algorithms have been developed, with more than 350 emerging in the last decade alone, reflecting both the inventive spirit in algorithm design and an ongoing debate surrounding the novelty of seemingly similar methodologies1. An important contribution of this paper is the presentation of a new taxonomy based on the number of control parameters in metaheuristic algorithms, which helps to clarify the relationships among diverse algorithmic strategies1. Key aspects such as hybridization strategies and AI-driven adaptations are discussed in depth, showing how intelligent modifications can lead to significant performance improvements—for instance, reducing air traffic complexity by 92.8% within a hyper-heuristic framework leveraging reinforcement learning5. The evolution of metaheuristics is contextualized within their growing applications in engineering, healthcare, energy, telecommunications, and urban planning, underscoring their practical importance. Overall, this review not only synthesizes historical developments but also provides insights into current trends and emerging directions in metaheuristic research, with the goal of guiding both veteran researchers and newcomers in the field.
Hybrid Metaheuristic Frameworks for Multi-Objective Engineering Optimization Problems
Hybrid metaheuristic frameworks have emerged as a dominant paradigm in addressing the complexities of multi-objective engineering optimization. Modern engineering design often demands the simultaneous optimization of conflicting objectives—such as minimizing cost while maximizing performance and reliability—under uncertain and nonlinear conditions. Traditional single-objective or standalone metaheuristics often exhibit limitations in exploration–exploitation balance, convergence stability, and robustness against uncertainty. To overcome these challenges, hybrid metaheuristics integrate multiple algorithmic strategies, combining the global exploration power of methods like the Gravitational Search Algorithm (GSA) with the local exploitation capability of techniques such as the Bat Algorithm (BAT), as exemplified by the MOGSABAT framework. This study provides a comprehensive examination of hybrid metaheuristic models for multi-objective optimization, discussing their theoretical underpinnings, mathematical formulations under uncertainty, and empirical performance. A systematic review of algorithmic architectures—including parallel, sequential, and machine-learning-assisted hybrids—is conducted, supported by rigorous statistical evaluation using Wilcoxon signed-rank tests and convergence-diversity metrics. Furthermore, the paper presents a detailed catalogue of metaheuristic algorithms and their hybridization potential for engineering applications. The findings demonstrate that hybrid metaheuristics not only outperform conventional algorithms in convergence speed and solution diversity but also offer enhanced scalability and resilience to data uncertainty. Finally, emerging trends such as adaptive hybridization, integration with machine learning, and parallelized implementations are identified as key directions for advancing future research in robust multi-objective optimization.