Working of Local Search Algorithm
As we are now clear with the basic concept of Local Search Algorithm in Artificial Intelligence, let's now discuss the working of Local Search Algorithm.

Step 1: The process starts to find the best solution. It moves forward with a random solution to the given problem.

Step 2: The objective function checks the quality of the initial solution, like the performance of the system, based on the problem constraints and needs.

Step 3: Heuristics helps the algorithm to find the neighbour solution by making small changes in the assumed solution. Heuristics is a technique to solve the problem using practical experiences.

Step 4: After selecting the neighbour solution, it is again checked with the help of the objective function. At last, the best solution is selected after repeating the same process again and again.

Step 5: The program stops after a certain criteria match. There are other ways also that can stop the program, such as the maximum time limit, the number of iterations exceeding, or a threshold value for the objective function.
 Step 6: The last solution at which the program stops is considered the best solution for the stated problem.
Types of Local Search Algorithm
There are basically three types of Local Search Algorithm present in Space Search Optimization. Let us discuss them.
Hill Climbing
Hill climbing is a type of Local Search Algorithm in Artificial Intelligence. This algorithm takes the reference of the greedy approach means it moves in the direction of optimized cost. You can get the best solution for travelling salesman problems with the help of the Hill Climbing method. The travelling salesman problem minimizes the distance covered by the man.
The pseudocode of the Hill Climbing algorithm is as follows.
def hill_climbing(problem):
current = node(problem.initialState)
while True:
neighbor = Successor of current with the highest objective function value
if neighbor.value <= current.value:
return current.state
current = neighbor
Explanation:
In the above code,

In line 1, we are assigning the initial state to the current.

Inside the while loop, we are taking only the successor value in the objective function.

If the value of its neighbour is not better than the current one, we are just returning the current node.

Else return the neighbour node.
The Hill Climbing algorithm has further three more variants. The names of those variants are as follows.
 Randomrestart Hill Climbing
 Firstchoice Hill Climbing
 Stochastic Hill Climbing
Local Beam Search
Local Beam Search is also a type of Local Search Algorithm and is based on the heuristic algorithm. The Local Beam Search algorithm is the updated version of the Hill Climbing algorithm. In this algorithm, the current node is the starting beam (set) of the k solutions rather than only a single solution.
The Local Beam Search algorithm creates a group of solutions from the current state for each iteration. After that, they evaluate the solution to choose the k best solution that later becomes the new current solution.
Simulated Annealing
The Simulated Annealing follows the rule of the heuristic algorithm to optimize and fix artificial intelligence problems. The main concept behind this algorithm is that it controls the randomness in the search process by changing the temperatures. High temperature leads to exploring new regions of the search space. This also increases its propensity to accept nonimproving moves.
The Simulated Annealing is a very successful algorithm as it solves many optimization problems easily. The problems that use the Simulated Annealing are as follows.
 Travelling Salesman Problem,
 Vehicle Routing Problem
 Job Shop Scheduling Problem
Pros of Local Search Algorithm
There are many advantages of using the Local Search Algorithm in Artificial Intelligence. Some of them are listed below.

It is a very efficient algorithm as it only needs to explore a small portion of the complete search space.

It takes less time compared to other space search algorithms.

It requires fewer conditions to be met than other search techniques.
 The code is easy in the local search algorithm, even for the complex question.
Cons of Local Search Algorithm
If there are many advantages to using the Local Search Algorithm in Artificial Intelligence, then there must be a few disadvantages also. Some of the cons are listed below.

The main disadvantage of the local search algorithm is that it gets trapped in the local optima.

If the cost function of the problem is high, then the schedule becomes slow.
 The local search algorithm can not tell the user that it got the optimal solution.
Frequently Asked Questions
Define Local Search Algorithm in Artificial Intelligence.
The Local Search Algorithm in Artificial Intelligence starts with a random solution. Later on, it performs minor changes until it finds the best solution.
How many types of Local Search Algorithm in Artificial Intelligence is present?
There are basically three types of Local Search Algorithm present that are Hill Climbing Algorithm, Local Beam Algorithm, and Simulated Annealing Algorithm.
What is the main advantage of using the Local Search Algorithm?
The main advantage of using the Local Search Algorithm is that it is very efficient. It also takes very less time, as compared to other search algorithms. Also, it has a very less number of conditions to be met.
What is the main disadvantage of using the Local Search Algorithm?
The main disadvantage of using the Local Search Algorithm is that it gets trapped in the local optima. The local search algorithm can not tell the user that it got the optimal solution.
Conclusion
This article discusses the topic of Local Search Algorithm in Artificial Intelligence. In detail, we have seen the definition of the Local Search Algorithm, its working, types, pros and cons.
We hope this blog has helped you enhance your knowledge of Local Search Algorithm in Artificial Intelligence. If you want to learn more, then check out our articles.
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