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=== 基本迭代The basic iteration ===

== 基本迭代The basic iteration ==

 

   

   

At each step, the simulated annealing heuristic considers some neighboring state ''s*'' of the current state ''s'', and [[probabilistic]]ally decides between moving the system to state ''s*'' or staying in-state ''s''.  These probabilities ultimately lead the system to move to states of lower energy.  Typically this step is repeated until the system reaches a state that is good enough for the application, or until a given computation budget has been exhausted.

At each step, the simulated annealing heuristic considers some neighboring state ''s*'' of the current state ''s'', and [[probabilistic]]ally decides between moving the system to state ''s*'' or staying in-state ''s''.  These probabilities ultimately lead the system to move to states of lower energy.  Typically this step is repeated until the system reaches a state that is good enough for the application, or until a given computation budget has been exhausted.

在每个步骤中，模拟退火这种启发式算法都会考虑当前状态s的某个相邻状态<math>s*<\math>，并概率性地决定系统的下一个状态是移至状态<math>s*<\math>还是保持状态<math>s<\math>。这些概率最终会使系统进入能量较低的状态。通常来说需要重复执行迭代步骤，直到系统达到足以满足应用程序的能量状态或耗尽了给定的计算预算。

在每个步骤中，模拟退火这种启发式算法都会考虑当前状态s的某个相邻状态<math>s*<\math>，并概率性地决定系统的下一个状态是移至状态<math>s*<\math>还是保持状态<math>s<\math>。这些概率最终会使系统进入能量较低的状态。通常来说需要重复执行迭代步骤，直到系统达到足以满足应用程序的能量状态或耗尽了给定的计算预算。

Optimization of a solution involves evaluating the neighbours of a state of the problem, which are new states produced through conservatively altering a given state. For example, in the [[travelling salesman problem]] each state is typically defined as a [[permutation]] of the cities to be visited, and its neighbours are the set of permutations produced by reversing the order of any two successive cities. The well-defined way in which the states are altered to produce neighbouring states is called a "move", and different moves give different sets of neighbouring states. These moves usually result in minimal alterations of the last state, in an attempt to progressively improve the solution through iteratively improving its parts (such as the city connections in the traveling salesman problem).

Optimization of a solution involves evaluating the neighbours of a state of the problem, which are new states produced through conservatively altering a given state. For example, in the [[travelling salesman problem]] each state is typically defined as a [[permutation]] of the cities to be visited, and its neighbours are the set of permutations produced by reversing the order of any two successive cities. The well-defined way in which the states are altered to produce neighbouring states is called a "move", and different moves give different sets of neighbouring states. These moves usually result in minimal alterations of the last state, in an attempt to progressively improve the solution through iteratively improving its parts (such as the city connections in the traveling salesman problem).