Simulated Annealing helps avoid getting stuck in a local minimum by probabilistically accepting worse solutions, especially in the early stages of the search. This acceptance is controlled by a parameter called 'temperature', which starts high and gradually decreases, allowing the algorithm to explore the solution space more broadly before converging.
Conditions: Requires an initial solution and a high initial temperature.; Uses a cooling schedule to gradually decrease the temperature.; Employs an acceptance criterion to decide whether to accept a worse solution based on the current temperature.
Simulated Annealing helps avoid getting stuck in a local minimum by probabilistically accepting worse solutions, especially in the early stages of the search. This acceptance is controlled by a parameter called 'temperature', which starts high and gradually decreases, allowing the algorithm to explore the solution space more broadly before converging.
Conditions: Requires an initial solution and a high initial temperature.; Uses a cooling schedule to gradually decrease the temperature.; Employs an acceptance criterion to decide whether to accept a worse solution based on the current temperature.