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.