Local search algorithm
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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.
Local search algorithm
- 1. LOCAL search ALGORITHMS 13 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. Subscribe
- 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. Subscribe 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 Subscribe
- 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. Subscribe 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. Subscribe
- 8. Thanks For Watching Next Topic: Genetic algorithms Reference: Artificial Intelligence A Modern Approach Third Edition Peter Norvig and Stuart J. Russell Subscribe Like Share
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