Heuristics (combinatorial optimization) is a class of algorithmic approaches and problem-solving techniques used to find approximate, high-quality solutions to complex combinatorial optimization problems. These problems involve selecting the best combination or permutation from a finite set of options, often under constraints, with the goal of optimizing an objective function. Heuristics are particularly valuable when finding an exact optimal solution is computationally intractable or impractical due to problem size or complexity, such as in NP-hard problems. They operate by trading off guaranteed optimality for computational speed and efficiency, aiming to achieve good, near-optimal solutions within reasonable timeframes. This approach is widely applied in domains like network optimization, resource allocation challenges in media marketing, and various computational tasks relevant to behavioral finance, where efficient, effective decision-making support is required for large-scale or time-sensitive problems.