Intelligent Real-Time Software Optimization Framework: Deep Learning–Enhanced Hybrid Fuzzy Model with WPM, TOPSIS, and PSO for Agentic Negotiation in Autonomous Systems
DOI:
https://doi.org/10.15662/IJEETR.2021.0306004Keywords:
Real-Time Software Optimization, AI-Driven Framework, Hybrid Fuzzy Model, Weighted Product Method (WPM), TOPSIS, Particle Swarm Optimization (PSO), Deep Learning, Agentic Negotiation, Autonomous Systems, Multi-Criteria Decision-Making, Adaptive SoftwareAbstract
Autonomous systems increasingly rely on real-time, intelligent software frameworks to ensure optimal performance, scalability, and adaptability. This research introduces an Intelligent Real-Time Software Optimization Framework that integrates Deep Learning with a hybrid fuzzy model combining Weighted Product Method (WPM), TOPSIS, and Particle Swarm Optimization (PSO). The framework is designed to support agentic negotiation among autonomous agents, enabling dynamic and context-aware decision-making in complex environments.
The hybrid fuzzy model effectively captures uncertainty and vagueness in multi-criteria decision-making, while WPM and TOPSIS systematically evaluate alternative strategies for software optimization. PSO dynamically tunes system parameters to enhance performance and minimize latency. Deep learning modules predict potential system bottlenecks and support adaptive software behavior in real time. The agentic negotiation framework ensures that autonomous components can coordinate and negotiate optimally, improving resource allocation, task scheduling, and system reliability.
Experimental results demonstrate significant improvements in response time, optimization efficiency, and autonomous agent coordination, validating the framework’s capability to advance real-time AI-driven software engineering in autonomous and distributed systems.
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