Insights Into Simulated Annealing
Insights Into Simulated Annealing
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模拟退火的见解
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Khalil Amine
中科院分区：
文献类型：
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作者：
Khalil Amine
模拟退火是一种用于全局组合优化问题的概率局部搜索方法，允许逐渐收敛到近乎最佳的解决方案。它由根据某些过渡规则从当前解决方案到更好的方法组成，同时偶尔接受一些上坡解决方案，以确保域探索的多样性并避免被当地的Optima捕获。该过程由控制迭代次数的一定静态或动态冷却时间表来管理。这种元哈疗法提供了一些优点，包括逃脱本地Optima的能力和使用少量短期内存的能力。迄今为止，由于其对许多组合和连续优化案例的适应性，以及其保证的渐近融合到全球最佳的情况下，迄今已出现了广泛的应用和变体。
Simulated annealing is a probabilistic local search method for global combinatorial optimisation problems allowing gradual convergence to a near-optimal solution. It consists of a sequence of moves from a current solution to a better one according to certain transition rules while accepting occasionally some uphill solutions in order to guarantee diversity in the domain exploration and to avoid getting caught at local optima. The process is managed by a certain static or dynamic cooling schedule that controls the number of iterations. This meta-heuristic provides several advantages that include the ability of escaping local optima and the use of small amount of short-term memory. A wide range of applications and variants have hitherto emerged as a consequence of its adaptability to many combinatorial as well as continuous optimisation cases, and also its guaranteed asymptotic convergence to the global optimum.