Conformational searching using a population-based incremental learning algorithm

A new population-based incremental learning algorithm for conformational searching of molecules is presented. This algorithm is particularly effective at determining, by relatively small number of energy minimizations, global energy minima of large flexible molecules. The algorithm is also able to find a large set of low energy conformations of more rigid small molecules. The performance of the algorithm is relation to other algorithm is examined via the test molecules: C(18) H(38) , C(39)H(80) , cycloheptadecane and a set of five drug-like molecules.     

Manager-worker-based model for the parallelization of quantum Monte Carlo on heterogeneous and homogeneous networks

A manager-worker-based parallelization algorithm for Quantum Monte Carlo (QMC-MW) is presented and compared with the pure iterative parallelization algorithm, which is in common use. The new manager-worker algorithm performs automatic load balancing, allowing it to perform near the theoretical maximal speed even on heterogeneous parallel computers. Furthermore, the new algorithm performs as well as the pure iterative algorithm on homogeneous parallel computers. When combined with the dynamic distributable decorrelation algorithm (DDDA) [Feldmann et al., J Comput Chem 28, 2309 (2007)], the new manager-worker algorithm allows QMC calculations to be terminated at a prespecified level of convergence rather than upon a prespecified number of steps (the common practice). This allows a guaranteed level of precision at the least cost. Additionally, we show (by both analytic derivation and experimental verification) that standard QMC implementations are not "perfectly parallel" as is often claimed.     

A fast annealing evolutionary algorithm for global optimization

By combining the aspect of population in genetic algorithms (GAs) and the simulated annealing algorithm (SAA), a novel algorithm, called fast annealing evolutionary algorithm (FAEA), is proposed. The algorithm is similar to the annealing evolutionary algorithm (AEA), and a very fast annealing technique is adopted for the annealing procedure. By an application of the algorithm to the optimization of test functions and a comparison of the algorithm with other stochastic optimization methods, it is shown that the algorithm is a highly efficient optimization method. It was also applied in optimization of Lennard-Jones clusters and compared with other methods in this study. The results indicate that the algorithm is a good tool for the energy minimization problem.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the bootstrap method

The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of coupling. The army ants algorithm is a form of rare event sampling whose efficiency is controlled by an input parameter. By choosing a suitable value of the input parameter the army ants algorithm can be reduced to the anteater algorithm (which is efficient for strongly coupled cases), and by optimizing the parameter the army ants algorithm may be efficiently applied to systems with low-probability events. To demonstrate the efficiency of the army ants algorithm, we performed atom-diatom scattering calculations on a model system involving weakly coupled electronic states. Fully converged quantum mechanical calculations were performed, and the probabilities for nonadiabatic reaction and nonreactive deexcitation (quenching) were found to be on the order of 10(-8). For such low-probability events the anteater sampling scheme requires a large number of trajectories ( approximately 10(10)) to obtain good statistics and converged semiclassical results. In contrast by using the new army ants algorithm converged results were obtained by running 10(5) trajectories. Furthermore, the results were found to be in excellent agreement with the quantum mechanical results. Sampling errors were estimated using the bootstrap method, which is validated for use with the army ants algorithm.     

An efficient sampling algorithm for variational Monte Carlo

We propose a new algorithm for sampling the N-body density mid R:Psi(R)mid R:(2)R(3N)mid R:Psimid R:(2) in the variational Monte Carlo framework. This algorithm is based upon a modified Ricci-Ciccotti discretization of the Langevin dynamics in the phase space (R,P) improved by a Metropolis-Hastings accept/reject step. We show through some representative numerical examples (lithium, fluorine, and copper atoms and phenol molecule) that this algorithm is superior to the standard sampling algorithm based on the biased random walk (importance sampling).     

An efficient and computational effective method for second order problems

An efficient and computational effective algorithm is introduced, for the first time in the literature, in the present paper. The main properties of the scheme are: (1) the algorithm is a two-step scheme, (2) the algorithm is symmetric one, (3) it is a hight algebraic order scheme (i.e of eight algebraic order), (4) it is a three-stages algorithm, (5) the first layer of the new method is based on an approximation to the point \(x_{n-1}\), (6) the scheme has vanished phase-lag and its first, second and third derivatives, (7) the new proposed algorithm has an interval of periodicity equal to \(\left( 0, 9.8 \right) \). For the present new scheme we study: (1) its construction, (2) its error analysis (3) its stability analysis. Finally, the investigation of the effectiveness of the new algorithm leads to its application to systems of differential equations arising from the Schrödinger equation.     

微分方程演化建模用于色谱保留时间的研究

利用演化算法的自适应、自组织、自学习的特性,设计了遗传程序设计与遗传算法相嵌套的混合演化建模算法,以遗传程序设计优化模型结构,以遗传算法优化模型参数,为化合物的液相色谱容量因子随流动相组成变化关系自动建立微分方程演化模型．通过对７个化合物的建模结果表明,演化模型的拟合和预测精度均明显高于常规的ＧＭ（１,１）模型和改进的ＧＭ（１,１）模型．     

Heuristic-based tabu search algorithm for folding two-dimensional AB off-lattice model proteins

The protein structure prediction problem is a classical NP hard problem in bioinformatics. The lack of an effective global optimization method is the key obstacle in solving this problem. As one of the global optimization algorithms, tabu search (TS) algorithm has been successfully applied in many optimization problems. We define the new neighborhood conformation, tabu object and acceptance criteria of current conformation based on the original TS algorithm and put forward an improved TS algorithm. By integrating the heuristic initialization mechanism, the heuristic conformation updating mechanism, and the gradient method into the improved TS algorithm, a heuristic-based tabu search (HTS) algorithm is presented for predicting the two-dimensional (2D) protein folding structure in AB off-lattice model which consists of hydrophobic (A) and hydrophilic (B) monomers. The tabu search minimization leads to the basins of local minima, near which a local search mechanism is then proposed to further search for lower-energy conformations. To test the performance of the proposed algorithm, experiments are performed on four Fibonacci sequences and two real protein sequences. The experimental results show that the proposed algorithm has found the lowest-energy conformations so far for three shorter Fibonacci sequences and renewed the results for the longest one, as well as two real protein sequences, demonstrating that the HTS algorithm is quite promising in finding the ground states for AB off-lattice model proteins.     

Global optimization of Lennard-Jones clusters by a parallel fast annealing evolutionary algorithm

A parallel fast annealing evolutionary algorithm (PFAEA) was presented and applied to optimize Lennard-Jones (LJ) clusters. All the lowest known minima up to LJ(116) with both icosahedral and nonicosahedral structure, including the truncated octahedron of LJ(38), central fcc tetrahedron of LJ(98), the Marks' decahedron of LJ(75)(-)(77), and LJ(102)(-)(104), were located successfully by the unbiased algorithm. PFAEA is a parallel version of fast annealing evolutionary algorithm (FAEA) that combines the aspect of population in genetic algorithm and annealing algorithm with a very fast annealing schedule. A master-slave paradigm is used to parallelize FAEA to improve the efficiency. The performance of PFAEA is studied, and the scaling of execution time with the cluster size is approximately cubic, which is important for larger scale energy minimization systems.     

Multi-mechanical properties comprehensive evaluation by single excitation mode using controlled laser air-force detection (CLAFD) technique

ABSTRACT

The controlled laser air-force detection (CLAFD) technique was developed to explore the feasibility of multi-mechanical property detection of polyurethane by single excitation mode. The adhesiveness, elastic modulus, hardness, resilience, and cohesiveness of polyurethane were predicted by the global variable partial least squares regression (Gv-PLSR) algorithm. Different preprocessing methods were used to preprocess the original laser data. The interval partial least squares regression (I-PLSR) algorithm was used to decrease the influences of the multicollinearity of the global laser variable and increase the stability of the multi-mechanical property prediction models. To further improve the prediction accuracy of the modeling of I-PLSR algorithm, the synergy interval PLSR (Si-PLSR) algorithm was used to combine the intervals with the higher evaluation index root-mean-square error of cross-validation (RMSECV) to predict the multi-properties. The results demonstrated that as a novel mechanical property detection technique, the CLAFD technique predicts in an efficient way. A suitable preprocessing method for the original laser data could greatly improve the effectiveness of prediction. The I-PLSR algorithm was used to improve the model’s stability significantly. Nevertheless, the prediction accuracy decreased. Comparing the I-PLSR algorithm with the Si-PLSR algorithm, the prediction accuracy and the modal stability were optimized by the latter. However, the accuracy was still lower than the Gv-PLSR algorithm. Therefore, the Gv-PLSR was the best algorithm to establish the multi-mechanical properties prediction model. This study provided a new comprehensive, nondestructive, and cross-contamination-free method to evaluate the comprehensive mechanical properties (adhesiveness, elastic modulus, hardness, resilience, and cohesiveness) of materials efficiently, especially for the soft materials such as biomaterial and food material.     

An algorithm for three-way data analysis that alternatively minimizes coupled vector (COV) resolution error and PARAFAC error.

A novel algorithm, alternatively minimizing coupled vector (COV) resolution error and PARAFAC error algorithm, is proposed in this paper. This algorithm can overcome the problem of slow convergence and is insensitive to the estimation of component number, such problems are unavoidable while using the traditional parallel factors analysis (PARAFAC) algorithm. In other words, this algorithm is capable of improving the computing speed and providing accurate resolutions provided that the number of factors used in the computation is no less than that of the actual underlying ones. The characteristic performances were demonstrated with a novel fluorescence data array.     

A new algorithm of two-electron repulsion integral calculations: a combination of Pople–Hehre and McMurchie–Davidson methods

A new algorithm of two-electron repulsion integral (ERI) calculations has been developed. In this algorithm, Cartesian axes are rotated to make several coordinate components zero or constant using the Pople–Hehre algorithm, and ERIs are evaluated at the rotated coordinate by the McMurchie–Davidson algorithm. The new algorithm applicable to (ss|ss) to (dd|dd) ERIs is implemented in the quantum chemistry program GAMESS. Test calculations show that the new algorithm reduces the computational times by 10–40%, as compared with the Pople–Hehre and the Rys quadrature algorithms.     

Direct dynamics simulations using Hessian-based predictor-corrector integration algorithms

In previous research [J. Chem. Phys. 111, 3800 (1999)] a Hessian-based integration algorithm was derived for performing direct dynamics simulations. In the work presented here, improvements to this algorithm are described. The algorithm has a predictor step based on a local second-order Taylor expansion of the potential in Cartesian coordinates, within a trust radius, and a fifth-order correction to this predicted trajectory. The current algorithm determines the predicted trajectory in Cartesian coordinates, instead of the instantaneous normal mode coordinates used previously, to ensure angular momentum conservation. For the previous algorithm the corrected step was evaluated in rotated Cartesian coordinates. Since the local potential expanded in Cartesian coordinates is not invariant to rotation, the constants of motion are not necessarily conserved during the corrector step. An approximate correction to this shortcoming was made by projecting translation and rotation out of the rotated coordinates. For the current algorithm unrotated Cartesian coordinates are used for the corrected step to assure the constants of motion are conserved. An algorithm is proposed for updating the trust radius to enhance the accuracy and efficiency of the numerical integration. This modified Hessian-based integration algorithm, with its new components, has been implemented into the VENUS/NWChem software package and compared with the velocity-Verlet algorithm for the H(2)CO-->H(2)+CO, O(3)+C(3)H(6), and F(-)+CH(3)OOH chemical reactions.     

A new single-well potential stochastic resonance algorithm to detect the weak signal

Based on the stochastic resonance theory, a new single-well potential stochastic resonance algorithm (SSR) to improve the signal-to-noise ratio (SNR) is presented. In the new algorithm, stochastic resonance takes place in a single-well potential driven only by the noise. The effect on the proposed algorithm is discussed. By using simulated and experimental data sets, it is proven that the signal-to-noise ratio (SNR) of the weak signal can be greatly enhanced by this method. The new single-well potential stochastic resonance algorithm (SSR) may be a promising tool to extend instrumental linear range and to improve the accuracy of trace analysis. The research enlarges the application scope of single-well potential to nonlinear signal processing.     

A new hybrid algorithm for finding the lowest minima of potential surfaces: approach and application to peptides

A new algorithm is presented for finding the global minimum, and other low-lying minima, of a potential energy surface (PES) of biological molecules. The algorithm synergetically combines three well-known global optimization methods: the diffusion equation method (DEM), which involves smoothing the PES; a simulated annealing (SA) algorithm; and evolutionary programming (EP), whose population-oriented approach allows for a parallel search over different regions of the PES. Tests on five peptides having between 6 and 9 residues show that the code implementing the new combined algorithm is efficient and is found to outperform the constituent methods, DEM and SA. Results of the algorithm, in the gas phase and with the GBSA implicit solvent model, are compared with crystallographic data for the test peptides; good accord is found in all cases. Also, for all but one of the examples, our hybrid algorithm finds a minimum deeper than those obtained by a very extensive scan. TINKERs implementation of the OPLS-AA force field is employed for the structure prediction. The results show that the new algorithm is a powerful structure predictor, when a reliable potential function is available. Our implementation of the algorithm is time-efficient, and requires only modest computational resources. Work is underway on applications of the new algorithm to structural prediction of proteins and other biological macro-molecules.     

A new approach for second‐order perturbation theory

A new second‐order perturbation theory (MP2) approach is presented for closed shell energy evaluations. The new algorithm has a significantly lower memory footprint, a lower FLOP (floating point operations) count, and a lower time to solution compared to previously implemented parallel MP2 methods in the GAMESS software package. Additionally, this algorithm features an adaptive approach for the disk/distributed memory storage of the MP2 amplitudes. The algorithm works well on a single workstation, small cluster, and large Cray cluster, and it allows one to perform large calculations with thousands of basis functions in a matter of hours on a single workstation. The same algorithm has been adapted for graphical processing unit (GPU) architecture. The performance of the new GPU algorithm is also discussed. © 2016 Wiley Periodicals, Inc.     

New recurrence relations for the rapid evaluation of electron repulsion integrals based on the accompanying coordinate expansion formula

We present an algorithm for the rapid computation of electron repulsion integrals (ERIs) over Gaussian basis functions based on the accompanying coordinate expansion (ACE) formula. The present algorithm uses equations termed angular momentum reduced expressions and introduces two types of recurrence relations to ACE formulas. Numerical efficiencies are assessed for (p pmid R:p p) and (sp spmid R:sp sp) ERIs by using the floating-point operation count. The algorithm is suitable for calculating ERIs for the same exponents but different angular momentum functions, such as L shells and derivatives of ERIs. The present algorithm is also capable of calculating ERIs with highly contracted Gaussian basis functions.     

加权最小二乘支持向量机改进算法及其在光谱定量分析中的应用

为克服异常训练样本对校正模型的负面影响,提出了一种加权最小二乘支持向量机(WLS-SVM)的改进算法,解决了原有算法存在的迭代收敛问题,并将其运用于光谱定量分析.实验结果表明:与原有算法相比,WLS-SVM改进算法显著增强了对异常样本的检测能力,并大幅度地提高了校正模型的稳健性.     

New method for parallel computation of Hessian matrix of conformational energy function in internal coordinates

A new algorithm for parallel calculation of the second derivatives (Hessian) of the conformational energy function of biomolecules in internal coordinates is proposed. The basic scheme of this algorithm is the division of the entire calculation of the Hessian matrix (called "task") into subtasks and the optimization of the assignment of processors to each subtask by considering both the load balancing and reduction of the communication cost. A genetic algorithm is used for this optimization considering the dependencies between subtasks. We applied this method to a glutaminyl transfer RNA (Gln-tRNA) molecule for which the scalability of our previously developed parallel algorithm was significantly decreased when the large number of processors was used. The speedup for the calculation was 32.6 times with 60 processors, which is considerably better than the speedup for our previously reported parallel algorithm. The elapsed time for the calculation of subtasks, data sending, and data receiving was analyzed, and the effect of the optimization using the genetic algorithm is discussed.