This method is ideal for continuous functions with a single minimum but is not suitable for global exploration of multi-minimum problems due to the propensity to get stuck in local minima. In these cases, (meta)heuristic approaches can provide approximate solutions where classical methods fail. Heuristic algorithms include simulated annealing, genetic algorithms, particle swarm optimisation, and ant colony optimisation. In each case, a budget is specified to limit the computational and time cost. Once the budget is used up, such as when the temperature in simulated annealing goes to zero, the searching process stops. These methods are more powerful and flexible than iterative methods because they can be applied to non-differentiable and non-continuous objective functions.