763
个编辑
更改
→相关算法 Related methods
有时候，回到一个比总是从当前状态移动更好的解决方案，而不是总是从当前状态移动。这个过程被称为模拟退火的重新启动。为此，我们将<code>s</code>和<code>e</code> </code> 设置为<code>sbest</code>和<code>ebest</code>并可能重新启动退火时刻表。重新启动的决定可能基于几个标准。其中值得注意的有:基于固定步数的重启，基于当前能量是否高于目前获得的最佳能量，随机重启等。
有时候，回到一个比总是从当前状态移动更好的解决方案，而不是总是从当前状态移动。这个过程被称为模拟退火的重新启动。为此，我们将<code>s</code>和<code>e</code> </code> 设置为<code>sbest</code>和<code>ebest</code>并可能重新启动退火时刻表。重新启动的决定可能基于几个标准。其中值得注意的有:基于固定步数的重启，基于当前能量是否高于目前获得的最佳能量，随机重启等。
==相关算法 Related methods==
==相关算法==
*[[交互Metropolis–Hasting算法]](又名顺序蒙特卡罗算法)<ref name=":3">{{Cite journal|title = Sequential Monte Carlo samplers - P. Del Moral - A. Doucet - A. Jasra - 2006 - Journal of the Royal Statistical Society: Series B (Statistical Methodology) - Wiley Online Library| doi=10.1111/j.1467-9868.2006.00553.x|volume=68| issue=3|journal=Journal of the Royal Statistical Society, Series B|pages=411–436|arxiv=cond-mat/0212648|year = 2006|last1 = Del Moral|first1 = Pierre| last2=Doucet| first2=Arnaud| last3=Jasra| first3=Ajay}}</ref>)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。
* [[Metropolis–Hastings algorithm|Interacting Metropolis–Hasting algorithms]] (a.k.a. [[Particle filter|Sequential Monte Carlo]]<ref name=":3">{{Cite journal|title = Sequential Monte Carlo samplers - P. Del Moral - A. Doucet - A. Jasra - 2006 - Journal of the Royal Statistical Society: Series B (Statistical Methodology) - Wiley Online Library| doi=10.1111/j.1467-9868.2006.00553.x|volume=68| issue=3|journal=Journal of the Royal Statistical Society, Series B|pages=411–436|arxiv=cond-mat/0212648|year = 2006|last1 = Del Moral|first1 = Pierre| last2=Doucet| first2=Arnaud| last3=Jasra| first3=Ajay}}</ref>) combined simulated annealing moves with an acceptance-rejection of the best fitted individuals equipped with an interacting recycling mechanism.
*[[量子退火 Quantum annealing]]使用“量子涨落 quantum fluctuations”而不是热涨落来穿过目标函数中高而薄的屏障。
* [[Quantum annealing]] uses "quantum fluctuations" instead of thermal fluctuations to get through high but thin barriers in the target function.
*[[随机隧穿 Stochastic tunneling]]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。
* [[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.
*[[禁忌搜索 Tabu search]]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。
* [[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.
*[[双阶段进化 Dual-phase evolution]]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。
* [[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.
*[[反应式搜索优化 Reactive search optimization]]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。
* [[LIONsolver|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.
*[[随机梯度下降法 Stochastic gradient descent]]从随机初始点开始进行许多贪婪搜索。
* [[Stochastic gradient descent]] runs many greedy searches from random initial locations.
*[[遗传算法 Genetic algorithms]] 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。
* [[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.
*[[模因算法 Memetic algorithms]]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。<ref>{{cite journal |last1=Moscato |first1=Pablo |title=An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search |journal=Annals of Operations Research |date=June 1993 |volume=41 |issue=2 |pages=85–121 |doi=10.1007/BF02022564 }}</ref> 模拟退火也被认为是一种增加搜索多样性的机制。<ref name=martial_arts>{{cite journal|last=Moscato|first=P.|year=1989|title=On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms|journal=Caltech Concurrent Computation Program|volume=|issue=report 826}}</ref>
* [[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.<ref>{{cite journal |last1=Moscato |first1=Pablo |title=An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search |journal=Annals of Operations Research |date=June 1993 |volume=41 |issue=2 |pages=85–121 |doi=10.1007/BF02022564 }}</ref> Annealing has also been suggested as a mechanism for increasing the diversity of the search <ref name=martial_arts>{{cite journal|last=Moscato|first=P.|year=1989|title=On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms|journal=Caltech Concurrent Computation Program|volume=|issue=report 826}}</ref>.
*[[逐步优化 Graduated optimization]]就是在优化时对目标函数进行“平滑”处理。
* [[Graduated optimization]] digressively "smooths" the target function while optimizing.
*[[蚁群优化 Ant colony optimization]](ACO)使用许多蚂蚁(或代理)遍历解空间，寻找局部生产区域。
* [[Ant colony optimization]] (ACO) uses many ants (or agents) to traverse the solution space and find locally productive areas.
*[[交叉熵方法 cross-entropy method]] (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。
* 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.
*[[和声搜索 Harmony search]] 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。
* [[Harmony search]] mimics musicians in improvisation process where each musician plays a note for finding a best harmony all together.
*[[随机优化 Stochastic optimization]]是一系列包括模拟退火和许多其他方法的算法集合。
* [[Stochastic optimization]] is an umbrella set of methods that includes simulated annealing and numerous other approaches.
*[[粒子群优化 Particle swarm optimization]] 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。
* [[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.
*分枝根算法 The runner-root algorithm (RRA)是一种元启发式优化算法，用于解决单峰和多峰问题,是由自然植物的生长和根启发而来。
* 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.
*[[智能水滴算法 Intelligent water drops algorithm]] (IWD)模仿自然水滴的行为来解决优化问题。
* [[Intelligent water drops algorithm]] (IWD) which mimics the behavior of natural water drops to solve optimization problems
*[[并行退火 Parallel tempering]]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。
*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。
*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。