ItemConference Paper

Quantum-Inspired Genetic Algorithms for Secure and Scalable Cloud-Based Decision Support Systems

Title
Quantum-Inspired Genetic Algorithms for Secure and Scalable Cloud-Based Decision Support Systems
Creator
Dsouza, Mithun; Pradhan, Rahul; Kaur, Chamandeep; Christal Mary, S.Suma; Preshiya, Jyothi; Ponni Valavan, M.
Description
Cloud-based DSS are critical for data-intensive decision-making but tremendously challenged by issues of scalability, security, and the optimization of resources. In general, optimization approaches such as GA, PSO, and ACO treat the problems of allocation and security enhancement of cloud resources very inadequately. Hence, the present work addresses developing a QIGA-based optimization framework for the performance optimization of cloud DSS. At its core, it utilizes quantum-like principles, such as superposition and probabilistic search, for resource optimization with respect to stability, security, and rapid convergence. Therefore, this underpinning framework comprises resource optimization, anomalous detection, and quantum-independent encryption through QIGA, which enhances the data security along with computational efficiency. The experimental results depict the performance efficiency of QIGA since its execution time, CPU, memory utilization requirements, and energy consumption are less while the task completion rate is higher and the security vulnerabilities are reduced in comparison to traditional optimization techniques. QIGA-based anomaly detection improves accuracy at the expense of response time, while its quantum-inspired encryption provides the best cryptographic security. Therefore, these results verify that QIGA is an efficient secured methodology for scalable cloud-controlled DSS, hence being a potential candidate for decision-making optimization in highly dynamic cloud environments. 2025 IEEE.
Source
2025 2nd International Conference on Integration of Computational Intelligent System, ICICIS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Anomaly Detection; Cloud Security; Cloud-Based Decision Support Systems; Quantum-Inspired Genetic Algorithm; Resource Optimization
Coverage
Dsouza M., CHRIST (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Pradhan R., GLA University, Department of Computer Engineering and Applications, Mathura, India; Kaur C., Jazan University, Computer Science & Information Technology Department, Jizan, Saudi Arabia; Christal Mary S.S., Chennai Institute of Technology, Department of AI & DS, Chennai, India; Preshiya J., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Department of ECE, Saveetha University, Tamil Nadu, Chennai, India; Ponni Valavan M., K.Ramakrishnan College of Engineering, Department of AI & DS, Tamil Nadu, Trichy, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833153736-4;
Format
online
Language
English
Type
Conference paper

Citation

Dsouza, Mithun; Pradhan, Rahul; Kaur, Chamandeep; Christal Mary, S.Suma; Preshiya, Jyothi; Ponni Valavan, M., “Quantum-Inspired Genetic Algorithms for Secure and Scalable Cloud-Based Decision Support Systems,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26011.

Output Formats