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This notion of slow cooling implemented in the simulated annealing algorithm is interpreted as a slow decrease in the probability of accepting worse solutions as the solution space is explored. Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the global optimal solution. In general, simulated annealing algorithms work as follows. The temperature progressively decreases from an initial positive value to zero. At each time step, the algorithm randomly selects a solution close to the current one, measures its quality, and moves to it according to the temperature-dependent probabilities of selecting better or worse solutions, which during the search respectively remain at 1 (or positive) and decrease towards zero.

This notion of slow cooling implemented in the simulated annealing algorithm is interpreted as a slow decrease in the probability of accepting worse solutions as the solution space is explored. Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the global optimal solution. In general, simulated annealing algorithms work as follows. The temperature progressively decreases from an initial positive value to zero. At each time step, the algorithm randomly selects a solution close to the current one, measures its quality, and moves to it according to the temperature-dependent probabilities of selecting better or worse solutions, which during the search respectively remain at 1 (or positive) and decrease towards zero.

模拟退火算法中实现的缓慢冷却的概念可被理解为探索解决方案空间时接受较差解决方案的概率缓慢下降。接受较差的解是元启发法的一个基本属性，因为它允许对全局最优解进行更广泛的搜索。一般来说，模拟退火算法的工作原理如下：温度从最初的正值逐渐降低到零。该算法在每个时间步长上随机选择一个接近当前解的解，测量其质量，并根据温度相关的概率选择更好或更坏的解，在搜索过程中分别保持在1(或正值)并向零递减。

模拟退火算法中实现的缓慢冷却的概念可被理解为探索解决方案空间时接受较差解决方案的概率缓慢下降。由于元启发法允许对全局最优解进行更广泛的搜索,所以接受较差的解可以说是元启发法的一个基本属性。一般来说，模拟退火算法的工作原理如下：温度从最初的正值逐渐降低到零。该算法在每个时间步长上随机选择一个接近当前解的解，测量其质量，并根据温度相关的概率选择更好或更坏的解，在搜索过程中分别保持在1(或正值)并向零递减。