What are the disadvantages of heuristic algorithms

Not guaranteed to find the optimal solution: Heuristic algorithms may find a good solution but cannot guarantee that it is the best possible solution.

Sensitivity to initial conditions: The performance of heuristic algorithms can be affected by the initial conditions and the choice of heuristic used.

Can get stuck in local optima: Heuristics may become trapped in local optima and fail to find the global optimum.

Limited scope of application: Heuristic algorithms are designed for specific problems and may not be applicable to other problems.

Difficulty in fine-tuning: Heuristic algorithms require careful fine-tuning to achieve optimal performance, which can be time-consuming and require expert knowledge.

Lack of transparency: Heuristic algorithms are often complex and difficult to interpret, making it hard to understand why a particular solution was chosen.