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Hill Climbing Algorithm in AI: Types, Features, and Applications

03rd Jan, 2024
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    Hill Climbing Algorithm in AI: Types, Features, and Applications

    In the field of artificial intelligence, the heuristic search algorithm known as "hill climbing" is employed to address optimization-related issues. The algorithm begins in a suboptimal state and incrementally improves it until a predetermined condition is satisfied. The empirical function serves as the basis for the required condition. The algorithm's goal is to get to an improved state called the optimal state from the current state. It attempts to continuously iterate (climb) until it achieves the peak value; thus, the name "Hill Climbing Algorithm" refers to the starting position, which is the non-optimal condition.

    On difficult optimization issues, local search techniques are employed to identify a candidate solution that maximizes the criterion. The set of all potential solutions inside the whole functional zone of a problem is referred to as a candidate solution. If you want to learn more about Artificial Intelligence and Machine Learning, Data Science Certification programs will help you advance your profession.

    What is a Hill Climbing Algorithm?

    • To discover the mountain's peak or the best solution to the problem, the hill climbing algorithm is a local search algorithm continuously advancing in the direction of increasing elevation or value. When it reaches a peak value where none of its neighbors have a greater value, it ends.
    • The hill climbing algorithm is a method for solving mathematical optimization issues. Traveling-salesman is one of the most cited instances of a hill-climbing algorithm. The problem where we need to cut down on the salesman's journey distance. 
    • Because it just searches inside its good immediate neighbor state and not further afield, it is also known as greedy local search. 
    • State and value make up the two components of a hill-climbing algorithm node.

    Large computational problems can be solved memory-effectively by using the hill climbing algorithm. It considers both the current state and the state immediately nearby. When we wish to optimize or decrease a certain function dependent on the input it is receiving, the hill climbing problem in artificial intelligence is extremely helpful.

    The "Traveling Salesman" Problem, in which we must reduce the salesman's journey distance, is the most popular hill climbing algorithm example in AI. Hill Climbing Algorithm is adept at efficiently locating local minima/maxima but may not discover the global optimal (best possible) solution.

    Hill climbing is a heuristic strategy, or to put it another way. It is a search technique or informed search technique that assigns various weights based on actual numbers to distinct nodes, branches, and destinations in a path. The search can now be improved using these statistics and the heuristic established in the hill climbing search in the AI model. The hill-climbing algorithm's key characteristics are its high input efficiency and superior heuristic assignment.

    How Does Hill Climbing Algorithm Work?

    The following steps are used by this algorithm to determine the best answer:

    • It tries to characterize the present situation as the starting point or initial state.
    • It searches for an ideal solution while generalizing the solution to the existing condition. The chosen answer might not be the ideal one.
    • It evaluates the generated solution in relation to the goal state, also known as the final state.
    • It will determine if the desired state has been attained or not. If this goal is not met, it will look for an alternative approach.

    Features of Hill Climbing

    1. Generate and Test variant: The Generate and Test method has an extension called Hill Climbing. Feedback from the Generate and Test approach aids in choosing which way to move through the search space.

    2. Greedy approach: The hill climbing in artificial intelligence in state space advances in the direction that best optimizes the output taken out in the solution-focused direction. It moves to the end to arrive at the solution while optimizing the cost of function.

    3. No backtracking: Backtracking to the prior state is not feasible since it cannot remember the system's previous state.

    4. Feedback mechanism: The program contains a feedback system that aids in choosing the movement's direction. The generate-and-test technique improves the feedback system.

    5. Incremental change: The algorithm makes small adjustments to the current solution.

    Types of Hill Climbing

    Following are the types of hill climbing in artificial intelligence:

    1. Simple Hill Climbing

    One of the simplest approaches is straightforward hill climbing. It carries out an evaluation by examining each neighbor node's state one at a time, considering the current cost, and announcing its current state. It seeks to find out how the following neighboring state is doing. It attempts to move if the success rate is higher than the current condition; otherwise, it remains in place. Although it is advantageous since it takes less time, the local optima have an impact on it. Therefore, it cannot always guarantee the best optimal solution.

    Algorithm for Simple Hill climbing:

    • Analyze the starting situation. Stop and return success if it's a goal state. If not, the initial state should be set as the current state.
    • Continue iterating until the solution state is reached or until no new operators are available to be applied to the current state.
    • Choose a state that hasn't yet been applied to the existing state, then do so to create a new state.
    • To assess the new state, carry out these.
    • Stop and return success if the current state is a goal state.
    • If it is superior to the current situation, make it the situation and go on.
    • Continue in the loop until a solution is found if it is not an improvement over the situation as it is.
    • Exit.

    2. Steepest-Ascent Hill Climbing

    A variant of the straightforward hill-climbing algorithm is the steepest-Ascent algorithm. This method looks at every node that borders the current state and chooses the one that is most near the goal state. This algorithm takes longer since it looks for more neighbors.

    Algorithm for Steepest Ascent Hill climbing:

    • Analyze the starting situation. Stop and return success if it's a goal state. If not, the initial state should be set as the current state.
    • Follow these instructions again and again until a solution is found, or the situation stays the same.
    • Choose a state that hasn't yet been used to modify the existing state.
    • Create a new "best state" that is initially equivalent to the existing state and then apply it to create the new state.
    • Execute these to assess the new state.
    • Stop and return success if the current state is a goal state.
    • If it is superior to the best state, make it the best state; otherwise, keep going by adding another new state to the loop.
    • Set the ideal situation as the current situation.
    • Exit.

    3. Stochastic hill climbing

    It is the exact opposite of the methods that were previously explained. With this method, the agent doesn't look up the values of nearby nodes. The agent chooses a neighboring node entirely at random, moves to that node, and then determines whether to continue this path based on the heuristic of that node. 

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    State-space Diagram for Hill Climbing and Analysis

    state-space diagram for hill climbing

    The optimization function and states are graphically represented in a state-space diagram. The local maximum and global maximum are what we seek to establish if the y-axis is the objective function. 

    The local minimum and the global minimum are what we seek to determine if the cost function reflects this axis. Here you can find more details regarding local minimum, local maximum, global minimum, and global maximum. A straightforward state-space diagram is shown in the diagram below. The state-space is represented by the x-axis, and the objective function has been plotted on the y-axis.

    Different Regions in the State Space Diagram

    1. Local Maximum: As the diagram makes clear, this is the state that is marginally superior to its neighboring states but never higher than the highest state.
    2. Global maximum: Its cost function value is at its highest, and it is the highest state in the state space.
    3. Current State: This is the condition in which an active agent is present.
    4. Flat local maximums are what happens when all the neighboring states have the same value and can be visualized as flat spaces (as shown in the diagram).
    5. Shoulder region: A region with an upward edge, it is also one of the issues with algorithms for climbing hills.

    Advantage of Hill Climbing Algorithm in Artificial Intelligence

    • Hill climbing in AI is a field that can be used continuously. Routing-associated issues, like portfolio management, chip design, and task scheduling, are advantageous. 
    • When you have a limited amount of computational capacity, hill climbing technique in AI is a useful solution for optimizing the difficulties. 
    • Compared to other search algorithms, this one is more effective. 
    • In terms of vehicle routing, automatic programming, circuit construction, etc., hill-climbing artificial intelligence processes are useful. 
    • It can address concerns with pure advancement, where the aim is to identify the most suitable state. 

    Problems in Different Regions in Hill climbing

    1. Local maximum

    All nearby states have a value that is worse than the present state when it reaches its local maximum. Since hill climbing search employs a greedy strategy, it won't progress to a worse state and end itself. Even though there might be a better way, the process will come to an end. 

    To get around the local maximum issue: Use the backtracking strategy. Keep track of the states you've visited. The search can go back to its initial setup and try a different route if it reaches an unpleasant condition. 

    2. Plateau

    All neighbors have the same value on the plateau. Therefore, choosing the ideal course is impossible. 

    To overcome plateaus: Break through plateaus by taking a huge leap. Choose a state that is far from the one you are in at random. 

    3. Ridge

    Any point on a ridge can appear as a peak since all directions of movement are downhill. As a result, the algorithm terminates in this condition. 

    To get over a Ridge: follow two or more rules before being tested. It suggests acting simultaneously in numerous directions. 

    Applications of Hill Climbing Algorithm

    1. Marketing

    A marketing manager can create the most effective marketing strategies with the use of a hill-climbing algorithm. The Traveling-Salesman algorithm is frequently employed to resolve these issues. It can be advantageous by reducing the distance travelled and enhancing travel times for sales team members. The algorithm effectively establishes the local minima. 

    2. Robotics

    The efficient operation of robotics benefits from hill climbing. It improves how well various robot systems and parts work together. 

    3. Job Scheduling

    Job scheduling has also used the hill climbing algorithm. This is the method by which resources on a computer system are distributed among various tasks. The migration of jobs from one node to a neighboring node allows for job scheduling. The appropriate migratory route is established using a hill-climbing technique.


    Hill climbing is a creative method used to address enormous computational difficulties. It can aid in determining the ideal response to issues. The field of artificial intelligence optimization could be revolutionized by this method. 

    It is a wise decision to make the AI implementation process easier. If hill climbing or simulated annealing don't work out well, you might select to have alternative approaches. At some point in their algorithm, they can utilize additional approaches like genetic algorithms and heuristic techniques (or the hyper-heuristic). 

    The hill climbing technique will be used in the future to tackle a variety of unique optimization issues with improved advanced features. 

    Learning AI and ML technologies can be done in a variety of ways, and the more the better. You can use these excellent resources as a jumping-off place to begin learning artificial intelligence and machine learning. With KnowledgHut’s Data Science with Python Course, you may advance your career and enter the world of cutting-edge technology.

    Frequently Asked Questions (FAQs)

    1Why is Hill Climbing Method Required When We Have Best-first Search?

    The best-first search determines the values of ALL nearby nodes before iterating with that node. When a neighboring node is found that is more valuable than the current node, simple hill climbing search algorithm iterates until it finds that node. 

    2How is Hill Climbing Different From A * Search Technique?

    Hill-climbing algorithms are less deliberate; instead of examining all open nodes, they expand the most promising descendent of the most recently extended node until they find a solution. Algorithms, such as A*, also take the distance of a node from the goal. 

    3Is Hill Climbing Algorithm Optimal?

    The results of hill climbing are not optimal, which is a significant disadvantage. Furthermore, it may become locked in directed search spaces with unidentified dead ends, preventing it from solving a problem that can be solved.


    Abhresh Sugandhi


    Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of, which offers multiple services in technical training, project consulting, content development, etc.

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