Cloud-Native AI Framework for Software Development Optimization: A Hybrid Fuzzy Integration of WPM, TOPSIS, Deep Learning, and Particle Swarm Optimization Algorithms
DOI:
https://doi.org/10.15662/IJARCST.2021.0405005Keywords:
Cloud-Native Computing, AI-Driven Software Development, Hybrid Fuzzy Framework, ; Weighted Product Method (WPM), TOPSIS, Particle Swarm Optimization (PSO), Deep Learning, Software Optimization, Multi-Criteria Decision-Making, Scalable Deployment, Intelligent Automation.Abstract
The rapid adoption of cloud-native architectures has transformed software development, necessitating intelligent frameworks for optimization, automation, and scalable deployment. This research presents a Cloud-Native AI Framework that integrates Deep Learning, the Weighted Product Method (WPM), and TOPSIS within a hybrid fuzzy and Particle Swarm Optimization (PSO) model to enhance software development processes.
The framework addresses multi-criteria decision-making challenges, resource allocation, and uncertainty in cloud-native environments. The hybrid fuzzy component captures vagueness and ambiguity in evaluating development strategies, while WPM and TOPSIS systematically rank alternatives based on performance, reliability, and cost. PSO further refines parameter selection, improving deployment efficiency and system responsiveness. Deep learning models predict system bottlenecks and optimize runtime performance in real time.
Experimental evaluation demonstrates substantial improvements in deployment speed, resource utilization, and automated decision-making accuracy, validating the framework’s capability to support scalable, cloud-native, AI-powered software development. This study contributes to next-generation software engineering by unifying AI, optimization algorithms, fuzzy reasoning, and cloud-native principles into a comprehensive, automated software optimization framework.
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