AI-Driven Optimization Techniques for Evolving Software Architecture in Complex Systems
Nicholas Richardson, Srinikhita Kothapalli, Abhishake Reddy Onteddu, RamMohan Reddy Kundavaram, Rajasekhar Reddy Talla
Abstract
This work uses AI-driven optimization to improve software design in complex systems by addressing scalability, flexibility, and performance while balancing conflicting goals. AI methods, including machine learning, reinforcement learning, and evolutionary algorithms, are studied to optimize architectural design and adaption in dynamic situations. The research synthesizes literature, case studies, and technical reports to assess AI-driven methodologies and find gaps in current practices using secondary data. AI approaches improve software system flexibility, scalability, and efficiency, especially multi-objective Optimization and hybrid methods. Data quality, computational costs, interpretability, and ethics still prevent mainstream usage. Policy implications emphasize the need for transparent, fair, and secure AI-driven optimization regulations. Addressing these difficulties and allowing responsible AI implementation requires promoting data governance, explainable AI standards, and business, academic, and government engagement. This paper emphasizes AI's transformational potential in software architecture evolution and calls for continuing research and policy creation to overcome present limits and lead future advances.