Informed search algorithm contains an array of knowledge such as how far we are from the goal, path cost, how to reach the goal node, etc. This knowledge helps agents to explore less to the search space and find more efficiently the goal node. The informed search algorithm is more useful for large search spaces. It uses the idea of heuristic, so it is also called Heuristic search. Heuristic function estimates how close the current state is to the goal. The heuristic method, however, might not always give the best solution, but it guarantees to find a good solution in a reasonable time. They can be classified to “local search” and “global search” methods.
Examples of informed algorithms: Tabu search, Hill climbing, Scatter search, Simulated annealing, Beam search, Particle swarm optimization, Greedy descent, Gradient search, Variable neighbourhood search, Guided local search, Genetic algorithms, Ant colony optimization, Greedy adaptive search procedure (GRASP), Greedy search, A*, Weighted A*, etc.