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Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem

机译:基于玻尔兹曼和玻色-爱因斯坦分布的多相模拟退火算法在蛋白质折叠问题中的应用

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摘要

A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distributions (MPSABBE) is proposed. MPSABBE was designed for solving the Protein Folding Problem (PFP) instances. This new approach has four phases: (i) Multiquenching Phase (MQP), (ii) Boltzmann Annealing Phase (BAP), (iii) Bose-Einstein Annealing Phase (BEAP), and (iv) Dynamical Equilibrium Phase (DEP). BAP and BEAP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions, respectively. DEP is also a simulated annealing search procedure, which is applied at the final temperature of the fourth phase, which can be seen as a second Bose-Einstein phase. MQP is a search process that ranges from extremely high to high temperatures, applying a very fast cooling process, and is not very restrictive to accept new solutions. However, BAP and BEAP range from high to low and from low to very low temperatures, respectively. They are more restrictive for accepting new solutions. DEP uses a particular heuristic to detect the stochastic equilibrium by applying a least squares method during its execution. MPSABBE parameters are tuned with an analytical method, which considers the maximal and minimal deterioration of problem instances. MPSABBE was tested with several instances of PFP, showing that the use of both distributions is better than using only the Boltzmann distribution on the classical SA.
机译:提出了一种新的基于Boltzmann和Bose-Einstein分布的混合多相模拟退火算法(MPSABBE)。 MPSABBE旨在解决蛋白质折叠问题(PFP)实例。这种新方法有四个阶段:(i)多淬火阶段(MQP),(ii)玻尔兹曼退火阶段(BAP),(iii)玻色-爱因斯坦退火阶段(BEAP)和(iv)动态平衡阶段(DEP)。 BAP和BEAP分别是基于Boltzmann和Bose-Einstein分布的模拟退火搜索过程。 DEP也是一种模拟的退火搜索程序,该程序在第四阶段的最终温度下应用,可以将其视为第二个Bose-Einstein相。 MQP是一个搜索过程,范围从极高到极高,采用非常快速的冷却过程,并且对接受新解决方案的限制不是很大。但是,BAP和BEAP的范围分别从高温到低温以及从低温到非常低的温度。他们对于接受新解决方案的限制更大。 DEP使用一种特殊的启发式方法,通过在执行过程中应用最小二乘法来检测随机均衡。 MPSABBE参数通过分析方法进行调整,该方法考虑了问题实例的最大和最小恶化。 MPSABBE在PFP的多个实例上进行了测试,结果表明,在经典SA上使用两种分布都比仅使用Boltzmann分布更好。

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