Real World Problem

Research on real-world problem-solving focuses on developing and applying advanced computational methods to tackle complex challenges across diverse domains. Current efforts center on refining metaheuristic algorithms like modified bat algorithms and ant nesting algorithms, alongside leveraging techniques such as answer set programming and machine learning integrated with forecasting and optimization, to improve solution efficiency and accuracy. These advancements aim to optimize solutions for problems ranging from traffic flow management and resource allocation to missing person identification and engineering design, ultimately contributing to more effective and efficient solutions in various sectors.