Title: A Unified Framework for Intelligent Resource Optimization and Behavioral Insight Using Evolutionary Computing and Web Intelligence
Authors: Md. Aftab and Aisha Said
Volume: 9
Issue: 3
Pages: 39-45
Publication Date: 2025/03/28
Abstract:
With the exponential growth of data, services, and users in web-based systems, optimizing resources and deriving actionable behavioral insights has become vital. This paper proposes a unified framework that combines web intelligence, evolutionary computing, and sentiment analysis to address complex challenges in resource allocation, testing effort distribution, and user behavior modeling. The framework integrates Genetic Algorithms (GA), Differential Evolution (DE), and machine learning (ML) techniques to enhance fault detection in modular systems, allocate cloud resources efficiently, and extract insights from social media data. By synthesizing findings from 40 state-of-the-art studies on software testing [1]-[10], web crawling [11]-[15], sentiment analysis [16]-[18], and fuzzy logic decision-making [19], this research presents a novel, holistic approach that enhances decision-making and system reliability. A conceptual implementation and cross-domain mapping validate the proposed architecture's flexibility and scalability. The results highlight improved optimization performance and system adaptability under dynamic constraints.