Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [simulated-annealing]

For questions related to the simulated annealing algorithm (SA), which is a probabilistic algorithm that attempts to find the global optimum of a function. SA can e.g. be used to solve the travelling salesman problem (TSP).

For more details, see e.g. https://en.wikipedia.org/wiki/Simulated_annealing.

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Are there local search algorithms that make use of memory to give better solutions?

I have been studying local search algorithms such as greedy hill-climbing, stochastic hill-climbing, simulated annealing, etc. I have noticed that most of these methods take up very little memory as compared to systematic search techniques. Are…
quantumcoder
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What is the difference between Stochastic Hill Climbing and Simulated Annealing?

I am reading about local search: hill climbing, and its types, and simulated annealing One of the hill climbing versions is "stochastic hill climbing", which has the following definition: Stochastic hill climbing does not examine for all its…
yaminoyuki
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When should I use simulated annealing as opposed to a genetic algorithm?

What kind of problems is simulated annealing better suited for compared to genetic algorithms? From my experience, genetic algorithms seem to perform better than simulated annealing for most problems.
Abbas Ali
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What is the basic purpose of local search methods?

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

In hill climbing methods, at each step, the current solution is replaced with the best neighbour (that is, the neighbour with highest/smallest value). In simulated annealing, "downhills" moves are allowed. What are the advantages of simulated…
Huma Qaseem
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What are examples of daily life applications that use simulated annealing?

In AIMA, 3rd Edition on Page 125, Simulated Annealing is described as: Hill-climbing algorithm that never makes “downhill” moves toward states with lower value (or higher cost) is guaranteed to be incomplete, because it can get stuck on a local…
Iram Shah
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What are most commons methods to measure improvement rate in a meta-heuristic?

When I run a meta-heuristics, like a Genetic Algorithm or a Simulated Annealing, I want to have a termination criterion that stops the algorithms when there is not any significant fitness improvement. What are good methods for that? I tried…
MScott
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Why does Simulated Annealing not take worse solution if the energy difference becomes higher?

In Simulated Annealing, a worse solution is accepted with this probability: $$p=e^{-\frac{E(y)-E(x)}{kT}}.$$ If that understanding is correct: Why is this probability function used? This means that, the bigger the energy difference, the smaller the…
MScott
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What is the difference between simulated annealing and deterministic annealing?

Not sure if this is the right place, but I was wondering if someone could briefly explain to me the differences & similarities between simulated annealing and deterministic annealing? I know that both methods are used for optimization and both…
Tinu
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What I Should Do to Reduce Solution Size for Simulated Annealing Algorithm?

I am trying to find the best solution for radar placement problem with using multi objective simulated annealing algorithm. So there is an area (in real map) and I want to put minimum count of radar as possible with the maximum area coverage. I am…
TarabydaVllasıCafcaflıAtArabsı
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