Difference Between Hill Climbing and Simulated Annealing Algorithm

Artificial Intelligence is a study of agents that act rationally. These agents often implement a search algorithm to find the goal.

A search algorithm consists of

• Goal state - final destination in the task
• search space - all available states of the problem
• initial state - the starting point of the problem

There are two sets of search algorithms: Uninformed Search, Informed Search

Uninformed search

This is also commonly known as blind search. In this type of search, no additional information is provided other than the problem definition. These algorithms can only differentiate between successors and the goal and non-goal state.

Informed Search

In this type of algorithm the agents have more information about the goal state. This information is obtained by something called a heuristic. A Heuristic is a function that gives information on how close we are to the goal state. Hill Climbing and simulated Annealing comes under in this category.

Hill Climbing

Hill climbing is a search algorithm used in artificial intelligence and optimization problems. It is a local search technique that continuously travels uphill, or in the direction of increasing value, in order to locate the peak or answer to an issue. It's referred to as "hill climbing" since it alludes to ascending a hill to the top.

Simulated Annealing

Simulated Annealing (SA) is an optimization algorithm inspired by the physical process of annealing in metallurgy, where materials are heated and then slowly cooled to remove defects and find a more stable state. The reason it is named "hill climbing" is that it is figuratively similar to climbing a hill to get to the top.

Simulated Annealing is a probabilistic technique used to find the global optimum of a function, especially when the search space is large and contains many local minima. It's a variation of the hill climbing algorithm but includes a mechanism to avoid getting stuck in local optima, allowing the algorithm to escape suboptimal solutions by allowing "bad" moves (i.e., moves that worsen the solution) at the beginning and gradually decreasing this likelihood over time.

Difference Between Hill Climbing and Simulated Annealing

The following table shows the difference between hill climbing and simulated annealing

Key	Hill Climbing	Simulated Annealing
Definition	Hill Climbing is a heuristic optimization technique that finds the best answer in agiven search space by repeatedly moving closer to a better solution at each stage.	Simulated Annealing is a probabilistic optimization technique that accepts less-than-ideal solutions with a preset probability in order to mimic the metallurgical annealing process and find the optimal solution in a given search region.
Implementation	Hill Climbing uses a greedy approach to iteratively progress towards the best response at each level. Only solutions that are better than the ones that are currently in place are accepted.	Simulated annealing uses a probabilistic approach to accept a worse solution with a given probability, allowing it to explore the search space and avoid local optimum. The chance of accepting a subpar response decreases as the algorithm progresses.
Local Optimum	Due to its vulnerability to getting trapped in local optima, Hill Climbing might not be able to find the global optimum.	Simulated annealing has a chance of escaping the local optimum and locating the global optimum.
Applications	Many different applications including control systems(Robotics), puzzles, image processing use Hill climbing	Various applications including logistics,data mining, circuit design use simulated annealing

Conclusion

The main difference between Hill climbing and simulated annealing is that even though both use iterative approaches to reach the goal state simulated annealing gives in to accept the worst solution possible in order to reach global optimum.