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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.

A 2-opt swap is a local search heuristic for the Traveling Salesman Problem. It involves removing two edges from the current tour and reconnecting the resulting paths in a different way to form a new tour.

Conditions: Applied to a valid TSP tour.; Involves exactly two edge removals and reconnections.

A 2-opt swap is a local search heuristic for the Traveling Salesman Problem. It involves removing two edges from the current tour and reconnecting the resulting paths in a different way to form a new tour.

Conditions: Applied to a valid TSP tour.; Involves exactly two edge removals and reconnections.