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.
The temperature parameter in Simulated Annealing controls the probability of accepting worse solutions during the search. Initially, the temperature is high, allowing the algorithm to explore a wide range of solutions, including those that degrade the current route.
Conditions: The algorithm is in the early stages of the search.; The temperature is initially high.; The temperature decreases over time according to a cooling schedule.
The temperature parameter in Simulated Annealing controls the probability of accepting worse solutions during the search. Initially, the temperature is high, allowing the algorithm to explore a wide range of solutions, including those that degrade the current route.
Conditions: The algorithm is in the early stages of the search.; The temperature is initially high.; The temperature decreases over time according to a cooling schedule.
Local Search optimization starts with an initial solution and iteratively applies small changes, such as 2-Opt swaps, to find a better one. Its main limitation is that it can get trapped in a local minimum, a state where no immediate improvement can be found, even though a globally better solution may exist.
Conditions: Requires an initial feasible solution.; Requires a defined neighborhood of solutions (e.g., all solutions reachable by one 2-Opt swap).; Iteratively moves to a better solution in the neighborhood until a local optimum is reached.
Local Search optimization starts with an initial solution and iteratively applies small changes, such as 2-Opt swaps, to find a better one. Its main limitation is that it can get trapped in a local minimum, a state where no immediate improvement can be found, even though a globally better solution may exist.
Conditions: Requires an initial feasible solution.; Requires a defined neighborhood of solutions (e.g., all solutions reachable by one 2-Opt swap).; Iteratively moves to a better solution in the neighborhood until a local optimum is reached.