Home | | **Artificial Intelligence** | Hill Climbing Search Algorithm: Concept, Algorithm, Advantages, Disadvantages

Hill climbing search algorithm is simply a loop that continuously moves in the direction of increasing value. It stops when it reaches a “peak” where no n eighbour has higher value.

**Hill Climbing**

Hill climbing search algorithm is simply a loop that continuously moves
in the direction of increasing value. It stops when it reaches a “peak” where
no n eighbour has higher value. This algorithm is considered to be one of the
simplest procedures for implementing heuristic search. The hill climbing comes
from that idea if you are trying to find the top of the hill and you go up
direction from where ever you are. This heuristic combines the advantages of
both depth first and breadth first searches into a single method.

The name hill climbing is derived from simulating the situation of a
person climbing the hill. The person will try to move forward in the direction
of at the top of the hill. His movement stops when it reaches at the peak of
hill and no peak has higher value of heuristic function than this. Hill
climbing uses knowledge about the local terrain, providing a very useful and
effective heuristic for eliminating much of the unproductive search space. It
is a branch by a local evaluation function. The hill climbing is a variant of
generate and test in which direction the search should proceed. At each point
in the search path, a successor node that appears to reach for exploration.

**Algorithm:**

**Step 1: **Evaluate the starting state. If it is a goal state then stop and return
success.

**Step 2: **Else, continue with the starting state as considering it as a current
state.

**Step 3: **Continue step-4 until a solution is found i.e. until there are no new
states left to be applied in the** **current
state.

**Step 4:**

a)
Select a state that has not been
yet applied to the current state and apply it to produce a new state.

b)
Procedure to evaluate a new
state.

i.
If the current state is a goal
state, then stop and return success.

ii.
If it is better than the current
state, then make it current state and proceed further.

iii.
If it is not better than the
current state, then continue in the loop until a solution is found.

**Step 5: **Exit.

**Advantages:**

Hill climbing technique is useful in job shop
scheduling, automatic programming, circuit designing, and vehicle routing and
portfolio management.

It is also helpful to solve pure optimization
problems where the objective is to find the best state according to the
objective function.

It requires much less conditions than other search
techniques.

**Disadvantages:**

The question that remains on hill climbing search is whether this hill
is the highest hill possible. Unfortunately without further extensive
exploration, this question cannot be answered. This technique works but as it
uses local information that’s why it can be fooled. The algorithm doesn’t
maintain a search tree, so the current node data structure need only record the
state and its objective function value. It assumes that local improvement will
lead to global improvement.

There are some reasons by which hill climbing often gets suck which are
stated below.

**Local Maxima:**

A local maxima is a state that is better than each of its neighbouring
states, but not better than some other states further away. Generally this
state is lower than the global maximum. At this point, one cannot decide easily
to move in which direction! This difficulties can be extracted by the process
of backtracking i.e. backtrack to any of one earlier node position and try to
go on a different event direction. To implement this strategy, maintaining in a
list of path almost taken and go back to one of them. If the path was taken that
leads to a dead end, then go back to one of them.

**Ridges:**

It is a special type of local maxima. It is a simply an area of search
space. Ridges result in a sequence of local maxima that is very difficult to
implement ridge itself has a slope which is difficult to traverse. In this type
of situation apply two or more rules before doing the test. This will
correspond to move in several directions at once.

**Plateau: **

It is a flat area of search space in which the neighbouringhave same
value. So it is very difficult to calculate the best direction. So to get out
of this situation, make a big jump in any direction, which will help to move in
a new direction this is the best way to handle the problem like plateau.

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Artificial Intelligence : Hill Climbing Search Algorithm: Concept, Algorithm, Advantages, Disadvantages |

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