Short answers, pulled from the story.
Local search operates as a heuristic method for solving computationally hard optimization problems. The process begins with a single candidate solution and moves iteratively to neighboring solutions within the search space.
Hill climbing emerges when an algorithm selects the neighbor that locally maximizes the criterion through greedy search strategies. This approach halts at a locally optimal point when no improving neighbors exist.
The 2-opt algorithm functions specifically for the traveling salesman problem. It seeks a cycle containing all nodes while minimizing total length by iterating on edge swaps.
Schuurman and Southey proposed three measures to assess effectiveness in 2001 within AI Journal volume 132 issue 2. Their work highlights that algorithms fail because they cannot see beyond their immediate horizon.
Local search does not guarantee that any given solution is optimal upon completion. The algorithm may stop even if the optimal solution lies far from the neighborhood of visited states.