<p>Hill climbing, Simulated annealing, genetic algorithms are different variants of local search algorithm. Hill Climbing use to find good solution for NP-hard problems. Simulated annealing was first used extensively to solve VLSI layout problems. It has been applied widely to factory scheduling and other large-scale optimization tasks. Genetic algorithm have had a wide-spread impact on optimization problems, such as circuit layout and job-shop scheduling.</p>

CC BY-SA 4.0
2018-11-24T15:28:24.673Z
2019-03-02T11:12:18.327Z
2019-03-02T11:12:18.327Z
Huma Qaseem
<p>I read about the hill climbing algorithms, the simulating annealing algorithm, but I am confused. What is the basic purpose of local search methods?</p>

<optimization><search><hill-climbing><simulated-annealing><local-search>
What is the basic purpose of local search methods?
Iram Shah
# What is the basic purpose of local search methods?

(Asked by: username_0 on 2018-11-19)

I read about the hill climbing algorithms, the simulating annealing algorithm, but I am confused. What is the basic purpose of local search methods?

**Tags:** optimization, search, hill-climbing, simulated-annealing, local-search


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## 1 answers

### Answer by username_1 on 2018-11-24. Score: 0 | Accepted answer

Hill climbing, Simulated annealing, genetic algorithms are different variants of local search algorithm. Hill Climbing use to find good solution for NP-hard problems. Simulated annealing was first used extensively to solve VLSI layout problems. It has been applied widely to factory scheduling and other large-scale optimization tasks. Genetic algorithm have had a wide-spread impact on optimization problems, such as circuit layout and job-shop scheduling.


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