Given that A* is one of the most popular graph-traversal and path-finding algorithms, it does find the shortest path from a start node toward a goal node. A* combines benefits of Dijkstra algorithms with Greedy Best-First-Search through cost consideration to reach the current node and some heuristic estimate of a cost to reach the goal.

A* maintains a heuristic function, assuming that an estimate from node is made of cost from a node to the goal; a cost function, referring to the exact cost from the start to. The algorithm uses the function to evaluate nodes. Nodes would be preferred on lowest values of, which can balance the discovery of the shortest path and proximity to the goal.

A* works best in scenarios where an optimal path is needed to be found, like routing, game development—including things like character movement—and robotics. Its efficiency depends basically on the choice of heuristic function. An admissible heuristic guaranteeing the optimality of the solution that A* finds will never overestimate the cost. In a setting in which heuristic functions are well-defined and computational resources reasonable, A* performs very well in giving optimal solutions for pathfinding efficiently.

the equation adding them here coz did not want the answer to look messy or difficult to understand : The algorithm evaluates nodes using the function $f\left(n\right)=g\left(n\right)+h\left(n\right)f(n)\; =\; g(n)\; +\; h(n)$f(n)=g(n)+h(n). It prioritizes nodes with the lowest $f\left(n\right)f(n)$f(n) value, effectively balancing the shortest path discovery and goal proximity.