Search algorithms are central to the problem-solving capabilities of artificial intelligence (AI) systems. Among these, Breadth-First Search (BFS) and Depth-First Search (DFS) are widely used for uninformed search problems. While BFS guarantees completeness and optimality, it suffers from high memory consumption, making it unsuitable for large-scale or resource-constrained applications. On the other hand, DFS offers a more memory-efficient solution but can miss optimal solutions and may fail in infinite or unbounded problem spaces. To address these limitations, we propose a hybridization of BFS and DFS that aims to combine the strengths of both algorithms. This hybrid approach leverages the optimality and completeness of BFS while maintaining the memory efficiency of DFS, thereby enhancing the overall problem-solving efficiency. In this paper, we provide a comprehensive theoretical analysis of the hybrid algorithm, followed by an implementation strategy and empirical evaluation. Through extensive experimentation in a variety of AI domains, including robotics, pathfinding, and game theory, we demonstrate that the hybrid BFS-DFS model significantly improves both time and space efficiency compared to traditional approaches. The results highlight the robustness and scalability of the hybrid model, making it a valuable tool for solving complex, dynamic problems in AI. Finally, we discuss potential future work to refine the approach and extend its applicability to real-world AI systems.

Keywords: Hybrid Search Algorithms, Breadth-First Search (BFS), Depth-First Search (DFS), Artificial Intelligence (AI), Problem-Solving Efficiency, Algorithm Optimization