<![CDATA[Uninformed search algorithms, specifically Breadth-First Search (BFS) and Depth-First Search (DFS), encounter significant scalability limitations when addressing complex problem spaces in modern Artificial Intelligence (AI) ecosystems. This study investigates the paradigm shift toward intelligent heuristic algorithms through a systematic literature review and comparative analysis of 24 recent academic sources. The evaluation focuses on three primary domains: logical problem solving, robotic navigation, and data infrastructure management. Results demonstrate that heuristic methods, such as A-Star and hybrid variants like PrunedBFS, offer superior time efficiency and memory optimization for autonomous navigation and massive computing tasks. Nevertheless, classic algorithms retain functional relevance for specific scenarios requiring exhaustive exploration. Furthermore, this study reveals that algorithmic evolution has fundamentally transformed digital infrastructure, driving a shift from Search Engine Optimization (SEO) to Answer Engine Optimization (AEO) and necessitating adaptive cybersecurity architectures. The research concludes that the future of AI development relies not on substitution, but on a collaborative synthesis integrating the robustness of classic methods with the adaptability of modern heuristics.]]>
Copyrights © 2026
