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<title>GNU Scientific Library – Reference Manual: Simulated Annealing algorithm</title>
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<a name="Simulated-Annealing-algorithm"></a>
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Next: <a href="Simulated-Annealing-functions.html#Simulated-Annealing-functions" accesskey="n" rel="next">Simulated Annealing functions</a>, Up: <a href="Simulated-Annealing.html#Simulated-Annealing" accesskey="u" rel="up">Simulated Annealing</a> [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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<a name="Simulated-Annealing-algorithm-1"></a>
<h3 class="section">25.1 Simulated Annealing algorithm</h3>
<p>The simulated annealing algorithm takes random walks through the problem
space, looking for points with low energies; in these random walks, the
probability of taking a step is determined by the Boltzmann distribution,
if
<em>E_{i+1} > E_i</em>, and
<em>p = 1</em> when
<em>E_{i+1} <= E_i</em>.
</p>
<p>In other words, a step will occur if the new energy is lower. If
the new energy is higher, the transition can still occur, and its
likelihood is proportional to the temperature <em>T</em> and inversely
proportional to the energy difference
<em>E_{i+1} - E_i</em>.
</p>
<p>The temperature <em>T</em> is initially set to a high value, and a random
walk is carried out at that temperature. Then the temperature is
lowered very slightly according to a <em>cooling schedule</em>, for
example: <em>T -> T/mu_T</em>
where <em>\mu_T</em> is slightly greater than 1.
<a name="index-cooling-schedule"></a>
<a name="index-schedule_002c-cooling"></a>
</p>
<p>The slight probability of taking a step that gives higher energy is what
allows simulated annealing to frequently get out of local minima.
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