改进的势能曲面变平法在二维非格点模型中的应用

蛋白质结构预测问题是生物信息学中的一个重要问题.缺少一种有效的全局寻优方法是阻碍这一问题解决的关键.势能曲面变平(ELP)法是一种启发式的全局优化方法,是一种推广的Monte Carlo方法,已成功地应用于许多优化问题.在ELP法的基础上,提出了改进的势能曲面变平(ELP )算法.将ELP 算法应用于二维非格点的蛋白质AB模型,预测和发现四条链长分剐为13,21,34和55的氨基酸序列的蛋白质结构.数值实验表明,ELP 算法是一种预测蛋白质结构的有效算法.     

一种禁忌搜索算法在二维HP非格模型中的应用

禁忌搜索算法是一种启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题,本文探讨将一种记忆的禁忌搜索算法应用于求解蛋白质结构预测问题。文中首先介绍了一种二维HP非格模型,此模型最后可以归结为一个全局优化问题,然后介绍了记忆的禁忌搜索算法在其中的应用,通过与PERM（Pruned—Enriched—Rosenbluth Method）比较,发现禁忌算法能得到较好的实验结果,经分析发现虽然二维HP非格模型很简单,但却能反映蛋白质结构的一些简单的性质,即在蛋白质结构中,疏水性氨基酸形成束,总是被极性氨基酸包围。数值实验表明该算法对于蛋白质结构预测是可行有效的。     

进化策略的一种改进及其在蛋白质结构预测中的应用

进化策略算法是一种模拟自然界生物进化过程的全局优化方法。本文将一种改进的进化策略算法应用于蛋白质三维HPNX非格模型,较成功地预测了蛋白质序列1RPB、1BPI和1UBQ的折叠趋势,说明了三维HPNX非格模型比简化HP非格模型更能准确地描述蛋白质的折叠情况,同时表明了进化策略算法用于蛋白质结构预测问题是可行的、有效的。     

基于多层量子粒子群优化算法和Toy模型的蛋白质结构预测研究

蛋白质结构预测,作为计算生物学基本问题之一,是个典型的NP难解问题.研究表明合理运用算法,借助物理模型,可用于预测蛋白质结构.Toy模型就是较为简单的类,其势能最低状态的确定则为结构预测的关键所在.量子粒子群算法是典型的智能优化算法,己广泛应用于多种系统寻优问题中.本篇文章提出使用1种改进的量子粒子群优化算法,并结合Toy模型,进行蛋白质结构预测.算法的改进在于对每次迭代的粒子,排序之后将种群分成精英子群、开采子群和勘探子群来区别处理,并通过实验进行运算和预测.结果表明运用改进的量子粒子群优化算法来进行蛋自质折叠结构预测是可行的且高效的.     

基于改进的禁忌搜索的蛋白质三维结构预测

禁忌搜索算法是一种局部搜索能力很强的全局迭代优化算法,已经被成功地应用到各种组合优化问题中。基于AB非格模型,该文将一种改进的禁忌搜索算法应用于蛋自质三维折叠结构预测。实验结果表明改进的禁忌算法求得的蛋白质三维最低能量构形的最低能量值比已有的算法求得的最低能量值要低,同时三维构形中形成了一个疏水核,被亲水残基包围,反映了真实蛋白质的结构特征。该算法效率高,可以有效地用于蛋白质三维折叠预测。     

基于Toy模型蛋白质折叠预测的多种群微粒群优化算法研究

基于Toy模型的蛋白质折叠结构预测问题是一个典型的NP问题.提出了多种群微粒群优化算法用于计算蛋白质能量最小值.该算法采用了一种新的算法结构,在该结构中,每一代的种群被分为精英子种群、开采子种群和勘探子种群三部分,通过改善种群的局部开采能力和全局勘探能力来提高算法的性能.分别采用Fibonacci蛋白质测试序列和真实蛋白质序列进行了折叠结构预测的仿真实验.实验结果表明该算法能够更精确地进行蛋白质折叠结构预测,为生物科学研究提供了一条有效途径.     

带静不平衡约束的矩形装填问题的启发式算法

卫星舱布局问题不仅是一个复杂的耦合系统设计问题,也是一个特殊的优化问题,具有NP难度性。解决这类问题最大的挑战在于需要优化的目标函数具有大量的被高能势垒分隔开的局部极小值点。Wang-Landau（WL）抽样算法是一种改进的蒙特卡罗方法,已经被成功地运用蛋白质结构预测等优化问题。本文以卫星舱布局优化问题为背景,首次将WL抽样算法引入矩形装填问题的求解。针对矩形装填物的特点,提出了启发式格局更新策略,以引导抽样算法在解空间中进行有效行走。为了加速搜索全局最优解,每次蒙特卡罗扫描生成新的布局时,便执行梯度法进行局部搜索。通过将局部搜索机制、启发式格局更新策略与WL抽样算法相结合,提出了一种用于解决带静不平衡约束的任意矩形装填问题的启发式布局算法。在布局优化过程中,通过在挤压弹性势能的基础上增加静不平衡量惩罚项并采用质心平移的方法,使布局系统的静不平衡量达到约束要求。另外,为了改进算法的搜索效率,提出了改进的有限圆族法用于装填物之间的干涉性判断和干涉量计算。通过对文献中两组共10个有代表性的算例进行实算,计算结果表明,所提出的装填算法是一种求解带静不平衡性能约束的任意矩形装填问题的有效算法。     

基于强化学习的HP模型优化方法研究

蛋白质结构预测问题一直是生物信息学中的重要问题。基于疏水极性模型的蛋白质二维结构预测问题是一个典型的NP难问题。目前疏水极性模型优化的方法有贪心算法、粒子群算法、遗传算法、蚁群算法和蒙特卡罗模拟方法等，但这些方法成功收敛的鲁棒性不高，容易陷入局部最优。由此提出一种基于强化学习的HP模型优化方法，利用其连续马尔可夫最优决策与最大化全局累计回报的特点，在全状态空间中，构建基于能量函数的奖赏函数，引入刚性重叠检测规则，充分挖掘生物序列中的全局进化关系，从而进行有效与稳定的预测。以3条经典论文序列和5条Uniref50序列为实验对象，与贪心算法和粒子群算法分别进行了鲁棒性、收敛性与运行时间的比较。贪心算法只能在62.5%的序列上进行收敛，该文方法能在5万次训练后稳定的在所有序列上达到了收敛。与粒子群算法相比，两者都能找到最低能量结构，但该文的运行时间较粒子群算法降低了63.9%。     

区间分割共轭梯度混沌优化算法及其应用

本文提出一种区间分割共轭梯度混沌优化算法(CSCGCOA)。新算法首先在全局搜索阶段采用混沌优化算法寻找一个次优解,寻优过程使用区间分割策略。进而以次优解为初值,局部搜索采用共轭梯度算法获得全局最优解。通过针对不同测试函数的仿真,并对比另外两个算法,结果表明新算法对初值不敏感,能有效得到全局最优解,同时具有很高的寻优速度。本文还将新算法应用于解决电力系统经济负荷分配问题,结果表明新算法是一种有效的高速算法。     

改进的粒子群算法求解蛋白质结构预测问题

蛋白质的生物学功能是由其空间结构决定的,因此,蛋白质结构预测就成为生物信息学领域中极具挑战性的问题之一.粒子群算法是一种新的群智能算法,优势在于简单容易实现,又有深刻的智能背景.在优化领域,粒子群算法适用 于求解连续优化问题,而基于HP格点模型的蛋白质结构预测问题是一个离散问题.因此,文中通过借鉴单点调整算法的思...     

An improved energy landscape paving algorithm for the problem of packing circles into a larger containing circle

The problem of packing circles into a larger containing circle is a kind of NP-hard problem. It is of high theoretical and practical value. Lacking powerful optimization method is the key obstacle to solving this problem. The energy landscape paving (ELP) method is a class of heuristic global optimization algorithm and a generation of Monte Carlo method. By incorporating new configuration update mechanism into ELP method, an improved energy landscape paving (ELP+) algorithm is put forward. The computational results, on two sets of instances taken from the literature, show the effectiveness of the proposed algorithm.     

A new energy landscape paving heuristic for satellite module layouts

This article describes a study of the satellite module layout problem (SMLP), which is a three-dimensional (3D) layout optimization problem with performance constraints that has proved to be non-deterministic polynomial-time hard (NP-hard). To deal with this problem, we convert it into an unconstrained optimization problem using a quasi-physical strategy and the penalty function method. The energy landscape paving (ELP) method is a class of Monte-Carlo-based global optimization algorithm that has been successfully applied to solve many optimization problems. ELP can search for low-energy layouts via a random walk in complex energy landscapes. However, when ELP falls into the narrow and deep valleys of an energy landscape, it is difficult to escape. By putting forward a new update mechanism of the histogram function in ELP, we obtain an improved ELP method which can overcome this drawback. By incorporating the gradient method with local search into the improved ELP method, a new global search optimization method, nELP, is proposed for SMLP. Two representative instances from the literature are tested. Computational results show that the proposed nELP algorithm is an effective method for solving SMLP with performance constraints.     

Basin filling algorithm for the circular packing problem with equilibrium behavioral constraints

With the background of the satellite module layout design, the circular packing problem with equilibrium behavioral constraints is a layout optimization problem and NP-hard problem in math. For lack of a powerful optimization method, this problem is hard to solve. The energy landscape paving (ELP) method is a class of stochastic global optimization algorithms based on the Monte Carlo sampling. Based on the quasiphysical strategy and the penalty function method, the problem is converted into an unconstrained...     

An efficient hybrid Taguchi-genetic algorithm for protein folding simulation

Given the amino-acid sequence of a protein, the prediction of a protein’s tertiary structure is known as the protein folding problem. The protein folding problem in the hydrophobic–hydrophilic lattice model is to find the lowest energy conformation. In order to enhance the performance of predicting protein structure, in this paper we propose an efficient hybrid Taguchi-genetic algorithm that combines genetic algorithm, Taguchi method, and particle swarm optimization (PSO). The GA has the capability of powerful global exploration, while the Taguchi method can exploit the optimum offspring. In addition, we present the PSO inspired by a mutation mechanism in a genetic algorithm. We demonstrate that our algorithm can be applied successfully to the protein folding problem based on the hydrophobic-hydrophilic lattice model. Simulation results indicate that our approach performs very well against existing evolutionary algorithm.     

基于Wang-Landau抽样的带静不平衡约束的简化卫星舱布局方法

以简化卫星舱承载板上三维布局设计问题为背景,研究一类带静不平衡约束的圆柱体和长方体混合待布物布局问题。针对该三维布局问题,将已成功应用于统计物理学和蛋白质结构预测的Wang-Landau抽样算法引入布局问题中。Wang- Landau抽样算法通过在复杂布局空间中进行有效抽样来得到一个平坦的能量直方图,从而精确估计布局系统的状态密度。通过将Wang- Landau抽样算法与带加速策略的最速下降法、质心平移策略相结合,提出了改进的Wang-Landau抽样算法。对文献中两个算例进行了实算,计算结果表明,改进的Wang-Landau抽样算法的收敛速度和解的质量相比文献中其它算法均有较大的提高。     

A multiple minima genetic algorithm for protein structure prediction

Protein structure prediction (PSP) has a large potential for valuable biotechnological applications. However the prediction itself encompasses a difficult optimization problem with thousands of degrees of freedom and is associated with extremely complex energy landscapes. In this work a simplified three-dimensional protein model (hydrophobic-polar model, HP in a cubic lattice) was used in order to allow for the fast development of a robust and efficient genetic algorithm based methodology. The new methodology employs a phenotype based crowding mechanism for the maintenance of useful diversity within the populations, which resulted in increased performance and granted the algorithm multiple solutions capabilities. Tests against several benchmark HP sequences and comparative results showed that the proposed genetic algorithm is superior to other evolutionary algorithms. The proposed algorithm was then successfully adapted to an all-atom protein model and tested on poly-alanines. The native structure, an alpha helix, was found in all test cases as a local or a global minimum, in addition to other conformations with similar energies. The results showed that optimization strategies with multiple solutions capability present two advantages for PSP applications. The first one is a more efficient investigation of complex energy landscapes; the second one is an increase in the probability of finding native structures, even when they are not at the global optimum.     

Determination of protein structure and dynamics combining immune algorithms and pattern search methods

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid Immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multiple–minima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms.