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→Related methods
* [[Parallel tempering]] is a simulation of model copies at different temperatures (or [[Hamiltonian (quantum mechanics)|Hamiltonian]]s) to overcome the potential barriers.
* [[Parallel tempering]] is a simulation of model copies at different temperatures (or [[Hamiltonian (quantum mechanics)|Hamiltonian]]s) to overcome the potential barriers.
*Interacting Metropolis–Hasting algorithms (a.k.a. Sequential Monte Carlo[12]) combined simulated annealing moves with an acceptance-rejection of the best fitted individuals equipped with an interacting recycling mechanism.
*Quantum annealing uses "quantum fluctuations" instead of thermal fluctuations to get through high but thin barriers in the target function.
*Stochastic tunneling attempts to overcome the increasing difficulty simulated annealing runs have in escaping from local minima as the temperature decreases, by 'tunneling' through barriers.
*Tabu search normally moves to neighbouring states of lower energy, but will take uphill moves when it finds itself stuck in a local minimum; and avoids cycles by keeping a "taboo list" of solutions already seen.
*Dual-phase evolution is a family of algorithms and processes (to which simulated annealing belongs) that mediate between local and global search by exploiting phase changes in the search space.
*交互Metropolis–Hasting算法(又名顺序蒙特卡罗[12]算法)结合模拟退火算法，采用交互循环机制，对最适合的个体进行接受-拒绝。
*量子退火使用“量子涨落”而不是热涨落来穿过目标函数中高而薄的屏障。
*随机隧穿尝试克服模拟退火运行在温度降低时从局部极小值逃逸的困难，通过“隧穿”通过障碍。
*禁忌搜索通常会移动到能量较低的邻近状态，但当它发现自己陷入局部极小值时，就会向上移动;并通过保留已经看到的解决方案的“禁忌列表”来避免循环。
*双阶段进化是一系列的算法和进程(模拟退火所属)，它们通过利用搜索空间中的相位变化在局部搜索和全局搜索之间进行协调。
*Reactive search optimization focuses on combining machine learning with optimization, by adding an internal feedback loop to self-tune the free parameters of an algorithm to the characteristics of the problem, of the instance, and of the local situation around the current solution.
*Stochastic gradient descent runs many greedy searches from random initial locations.
*Genetic algorithms maintain a pool of solutions rather than just one. New candidate solutions are generated not only by "mutation" (as in SA), but also by "recombination" of two solutions from the pool. Probabilistic criteria, similar to those used in SA, are used to select the candidates for mutation or combination, and for discarding excess solutions from the pool.
*Memetic algorithms search for solutions by employing a set of agents that both cooperate and compete in the process; sometimes the agents' strategies involve simulated annealing procedures for obtaining high quality solutions before recombining them.[13] Annealing has also been suggested as a mechanism for increasing the diversity of the search [14].
反应式搜索引擎优化的重点是将机器学习和优化结合起来，通过增加一个内部反馈回路来自动调整算法的自由参数，使其适应问题、实例和当前解决方案周围的局部情况的特点。
随机梯度下降从随机的初始位置运行许多贪婪的搜索。
新的候选解决方案不仅产生“变异”(如在 SA) ，而且还“重组”的两个解决方案从池。
有时代理人的策略涉及在重组之前获得高质量解决方案的模拟退火。 退火也被认为是增加搜索多样性的一种机制[14]。
*反应式搜索优化侧重于将机器学习与优化结合起来，通过添加一个内部反馈回路，自调整算法的自由参数，根据问题、实例，去适应当前解决方案周围的局部情况。
*随机梯度下降法从随机初始点开始进行许多贪婪搜索。
*遗传算法保持了一个解决方案池，而不仅仅是一个。
*新的候选解决方案不仅可以通过“突变”(如SA)生成，还可以通过从池中“重新组合”两个解决方案生成。 与 SA 中使用的概率标准类似，用于选择突变或组合的候选方案，并从池中剔除多余的解。
*模因算法通过雇佣一组在过程中既合作又竞争的代理人来寻找解决方案;
*有时，代理商的策略包括模拟退火程序，以获得高质量的解决方案，再进行重新组合。[13]模拟退火也被认为是一种增加搜索[14]多样性的机制。
*Graduated optimization digressively "smooths" the target function while optimizing.
*Ant colony optimization (ACO) uses many ants (or agents) to traverse the solution space and find locally productive areas.
*The cross-entropy method (CE) generates candidates solutions via a parameterized probability distribution. The parameters are updated via cross-entropy minimization, so as to generate better samples in the next iteration.
*Harmony search mimics musicians in improvisation process where each musician plays a note for finding a best harmony all together.
*Stochastic optimization is an umbrella set of methods that includes simulated annealing and numerous other approaches.
*逐步优化就是在优化时对目标函数进行“平滑”处理。
*蚁群优化(ACO)使用许多蚂蚁(或代理)遍历解空间，寻找局部生产区域。
*交叉熵方法(CE)通过参数化的概率分布生成候选解。通过交叉熵最小化对参数进行更新，以便在下一次迭代中生成更好的样本。
*和声搜索模仿音乐家在即兴创作的过程中，每个音乐家演奏一个音符以找到最好的和声。
*随机优化是一系列包括模拟退火和许多其他方法的算法集合。
*Particle swarm optimization is an algorithm modelled on swarm intelligence that finds a solution to an optimization problem in a search space, or model and predict social behavior in the presence of objectives.
*The runner-root algorithm (RRA) is a meta-heuristic optimization algorithm for solving unimodal and multimodal problems inspired by the runners and roots of plants in nature.
*Intelligent water drops algorithm (IWD) which mimics the behavior of natural water drops to solve optimization problems
*Parallel tempering is a simulation of model copies at different temperatures (or Hamiltonians) to overcome the potential barriers.
*粒子群优化是一种以群体智能为模型的算法，它可以在搜索空间中找到优化问题的解决方案，或者在目标存在的情况下对社会行为进行建模和预测。
*分枝根算法(RRA)是一种元启发式优化算法，用于解决单峰和多峰问题的启发，在自然植物的运行和根。
*智能水滴算法(IWD)模仿自然水滴的行为来解决优化问题。
*并行退火是模拟模型副本在不同温度(或汉密尔顿)下克服潜在障碍的过程。
==另请参阅==
*自适应模拟退火
*马尔可夫链
*组合优化
*双相演化
*自动标签位置
*多学科优化
*地点和路线
*分子动力学
*旅行商问题
*计算机视觉中的图形切割
*粒子群优化
*智能水滴算法
==See also==
==See also==