
Simulated Annealing
Simulated annealing(模拟退火)是一种概率性算法,常用于优化问题,尤其在组合优化和全局优化领域。该算法模拟金属退火过程中原子冷却的过程,以寻找问题的最优解。
词语辨析
在英语中,“simulated annealing”主要作为一个名词使用,指代一种特定的算法。它没有形容词形式,因此不存在形容词和名词的不同含义。
近义词与反义词
- 近义词:优化算法(Optimization Algorithm)、遗传算法(Genetic Algorithm)
- 反义词:确定性算法(Deterministic Algorithm)
柯林斯词典与牛津词典中的定义
- 柯林斯词典:
Simulated annealing is a method for solving optimization problems by probabilistically searching for the best solution.
- 牛津词典:
Simulated annealing is an algorithm that mimics the cooling process of metals to find a near-optimal solution.
用法
在计算机科学和运筹学中,simulated annealing 被广泛应用于求解复杂的优化问题,如旅行商问题、排程问题等。
例句
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The simulated annealing algorithm is effective for finding global minima.
模拟退火算法对于寻找全局最小值是有效的。
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By using simulated annealing, we can escape local optima.
通过使用模拟退火,我们可以逃避局部最优解。
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The process involves several iterations of simulated annealing.
这个过程涉及多次模拟退火的迭代。
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In many cases, simulated annealing provides a good balance between exploration and exploitation.
在许多情况下,模拟退火在探索和利用之间提供了良好的平衡。
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Researchers often compare simulated annealing with other optimization techniques.
研究人员常常将模拟退火与其他优化技术进行比较。
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Simulated annealing can be particularly useful for combinatorial problems.
模拟退火对于组合问题尤其有用。
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The algorithm's performance depends on the cooling schedule used.
算法的性能取决于使用的降温计划。
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Implementing simulated annealing requires careful tuning of parameters.
实施模拟退火需要对参数进行仔细调整。
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In the field of artificial intelligence, simulated annealing is a popular technique.
在人工智能领域,模拟退火是一种流行的技术。
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Simulated annealing can be applied to image processing tasks.
模拟退火可以应用于图像处理任务。
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Many software packages include simulated annealing as a built-in function.
许多软件包将模拟退火作为内置功能。
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The key to successful simulated annealing is to balance exploration and exploitation.
成功的模拟退火的关键是平衡探索和利用。
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Simulated annealing is often used in machine learning to optimize hyperparameters.
模拟退火通常用于机器学习中优化超参数。
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Adjusting the initial temperature is crucial for the efficiency of simulated annealing.
调整初始温度对模拟退火的效率至关重要。
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Simulated annealing can yield satisfactory results in large-scale problems.
模拟退火在大规模问题中可以产生令人满意的结果。
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Understanding the theoretical foundation of simulated annealing can enhance its application.
理解模拟退火的理论基础可以增强其应用。
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The convergence rate of simulated annealing can vary depending on the problem.
模拟退火的收敛速率可能因问题而异。
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Parameters such as cooling rate significantly affect the performance of simulated annealing.
降温速率等参数显著影响模拟退火的性能。
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Simulated annealing is a robust method for solving non-convex optimization problems.
模拟退火是解决非凸优化问题的稳健方法。