A Comprehensive Review of Evaluating the Efficiency of Solving Time for Two-Dimensional Computational Problems

Abstract

The growing importance of tackling 2D computational problems is examined in this research. These issues are crucial in computer graphics, robotics, medical imaging, and autonomous systems. In real-time applications that require quick and accurate decision-making, balancing computer efficiency and accuracy is crucial. These applications are especially affected by rising technology expectations. After recent advances in parallel computing, machine learning, and hardware acceleration, this study examines the latest two-dimensional problem-solving methods and their shifting environment. It illustrates both progress and problems that must be solved to maximize performance across several domains. This study examines two-dimensional computational problem-solving methodologies, strategies, and algorithms in detail, focusing on time efficiency. To identify published material, all indexed databases—ScienceDirect, ResearchGate, and Google Scholar—were searched. The criteria were used to choose 2020–2025 research based on publication names, study designs, interventions, and findings. The topic has seventy-nine relevant articles. The data analysis identified significant sentences pertinent to the coding framework, highlighted notable discoveries, and created a narrative assessment framework. The following table summarizes various studies comparing heuristic algorithms from different countries. German researchers compared beam search techniques (BS1, BS2, and BS3) to dynamic programming. They found that these algorithms improve speed and accuracy as item categories rise. A Chinese study found that a bandit-based system that incorporates multiple intelligent optimization strategies performed as well as the best current methods. Thai, Indian, Pakistani, and Iranian academics suggested self-sampling, heat transfer, and wavelet analysis algorithms for handling various problems. These works demonstrate the advances gained in computational techniques across a wide range of subject topics, each with its own challenges and computational complexity. The experiments found that heuristic methods, particularly beam search approaches, can reduce computation time compared to dynamic programming. More item types lead to a balance between accuracy and speed. Bandit algorithms have shown that adaptable frameworks can handle complex problems. Heuristics, parallel computing, and machine learning can increase performance and scalability for increasingly complex computational problems.