Real-Time AI-Driven Software Development: Hybrid Fuzzy WPM and TOPSIS Integration with Deep Learning and Particle Swarm Optimization in Agentic Negotiation Frameworks
DOI:
https://doi.org/10.15680/IJCTECE.2020.0405005Keywords:
Real-Time Software Development, AI-Driven Optimization, Hybrid Fuzzy Framework, Weighted Product Method (WPM), TOPSIS, Particle Swarm Optimization (PSO), Deep Learning, Agentic Negotiation Framework, Autonomous Software Agents, Multi-Criteria Decision-Making, Software ScalabilityAbstract
The increasing complexity of modern software systems, particularly in real-time and multi-agent environments, necessitates intelligent frameworks that optimize decision-making, performance, and adaptability. This research proposes a Real-Time AI-Driven Software Development Framework that integrates Weighted Product Method (WPM) and TOPSIS within a hybrid fuzzy logic model, enhanced by Deep Learning and Particle Swarm Optimization (PSO). The framework is designed to support agentic negotiation mechanisms, enabling autonomous agents to make optimized, context-aware decisions in real time.
The hybrid fuzzy model manages uncertainty and vagueness inherent in software development parameters, while WPM and TOPSIS provide a structured multi-criteria evaluation of development strategies. PSO dynamically optimizes system parameters and resource allocation, and deep learning modules predict performance bottlenecks, enabling self-adaptive decision-making. The agentic negotiation framework allows autonomous components to coordinate and negotiate effectively, ensuring optimal task allocation and software deployment in dynamic environments.
Experimental evaluations demonstrate improvements in deployment efficiency, real-time decision accuracy, and system scalability, confirming the framework’s potential to advance intelligent, automated software engineering in complex, multi-agent contexts
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