Richa Singh,
- Research Scholar, Department of Commerce, Banaras Hindu University, Varanasi, Uttar Pradesh, India
Abstract
In the era of intelligent technologies and data-driven systems, multi-objective decision-making (MODM) has become an essential aspect of managing complex environments such as smart cities, autonomous systems, and cyber-physical networks. As decision-making scenarios become increasingly dynamic and uncertain, there is a growing need for advanced methodologies that can handle diverse objectives, conflicting constraints, and incomplete information. This review highlights the emerging role of entropy, symmetry, and data fusion as foundational pillars in addressing these challenges. Entropy, originating from information theory, is widely used to quantify uncertainty, enabling decision-makers to assign weights objectively and evaluate the diversity of outcomes. Symmetry, often associated with pattern recognition and mathematical invariance, offers structural simplification, improves computational efficiency, and reveals hidden relationships within system components. Meanwhile, data fusion techniques integrate heterogeneous data sources to produce unified, coherent, and context-rich insights that enhance decision quality. Together, these methods form a powerful triad that supports intelligent and adaptive decision systems. We explore recent developments in entropy-based weighting strategies, symmetry-guided optimization models, and multi-level data fusion architectures. Furthermore, we analyze their synergistic impact on various domains, including smart manufacturing, autonomous control, sensor networks, and intelligent transportation systems. By combining theoretical foundations with practical applications, this review provides a comprehensive understanding of how these methods can be integrated to enhance robustness, accuracy, and adaptability in MODM. he paper also identifies critical challenges such as computational complexity, scalability, and interpretability, while outlining promising future directions, including hybrid AI models, real-time fusion frameworks, and symmetry-aware deep learning. Ultimately, the fusion of entropy, symmetry, and data-driven intelligence represents a transformative shift in the design and optimization of smart systems, paving the way for more informed, balanced, and resilient decision-making in increasingly complex environments.
Keywords: Entropy, symmetry, data fusion, multi-objective decision-making, smart systems, uncertainty quantification
[This article belongs to Emerging Trends in Symmetry ]
Richa Singh. Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems. Emerging Trends in Symmetry. 2025; 01(02):44-49.
Richa Singh. Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems. Emerging Trends in Symmetry. 2025; 01(02):44-49. Available from: https://journals.stmjournals.com/etsy/article=2025/view=233708
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| Volume | 01 |
| Issue | 02 |
| Received | 15/06/2025 |
| Accepted | 20/08/2025 |
| Published | 08/12/2025 |
| Publication Time | 176 Days |
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