Local search algorithms operate by examining the current node and its neighbors. They are suitable for problems where the solution is the goal state itself rather than the path to get there. Hill-climbing and simulated annealing are examples of local search algorithms. Hill-climbing continuously moves to higher value neighbors until a local peak is reached. Simulated annealing also examines random moves and can accept moves to worse states based on probability. Both aim to find an optimal or near-optimal solution but can get stuck in local optima.

1. LOCAL search ALGORITHMS
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Hill-climbing search
Simulated Annealing

2. Local Search Algorithms
Local search algorithms operate using a single current node and generally move only to
neighbours of that node.
Local search method keeps small number of nodes in memory . They are suitable for
problems where the solution is the goal state itself and not the path.
In addition to finding goals, local search algorithms are useful for solving pure optimization
problems, in which the aim is to find the best state according to an objective function.
Hill-climbing and simulated annealing are examples of local search algorithms.
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3.  Hill climbing algorithm is a local search
algorithm which continuously moves in
the direction of increasing elevation/value
to find the peak of the mountain or best
solution to the problem. It terminates
when it reaches a peak value where no
neighbor has a higher value.
 Hill climbing is sometimes called greedy
local search because it grabs a good
neighbor state- without thinking ahead
about where to go next.
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Hill-climbing search

4. Limitations:
Hill climbing cannot reach the optimal/best state(global maximum) if
it enters any of the following regions :
• A local maximum is a peak that is higher than
each of its neighbouring states but lower than
the global maximum.
Local
• A plateau is a flat area of the state-space
landscape. It can be a flat local maximum, from
which no uphill exit exists, or a shoulder, from
which progress is possible.
Plateaus
• A Ridge is an area which is higher than
surrounding states, but it can not be reached
in a single move.
Ridges
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5. Subscribe
A Ridges is shown in figure result in a sequence of local
maxima that is very difficult for greedy algorithm to
navigate.

6. Variations of Hill Climbing
 In Steepest Ascent hill climbing all
successors are compared and the
closest to the solution is chosen.
Steepest ascent hill climbing is like
best-first search, which tries all
possible extensions of the current
path instead of only one.
 It gives optimal solution but time
consuming.
 Also known as Gradient search.
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Current node
Successor node
Jump
Local
Maxima
Global
Maxima

7. Simulated
Annealing
 Annealing is the process used to temper or
harden metals and glass by heating them to a
high temperature and then gradually cooling
them, thus allowing the material to reach a low
energy crystalline state.
 The simulated annealing algorithm is quite
similar to hill-climbing. Instead of picking the
best move, however, it picks a random move.
If the move improves the situation , it is always
accepted. Otherwise the algorithm accepts the
move with some probability less than 1.
 Checks all the neighbors.
 Moves to worst state may be accepted.
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8. Thanks For
Watching
Next Topic: Genetic algorithms
Reference:
Artificial Intelligence
A Modern Approach Third Edition
Peter Norvig and Stuart J. Russell
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