Graph attention network for global search of atomic clusters:A case study of Ag_(n)(n=14-26)clusters

Due to coexistence of huge number of structural isomers,global search for the ground-state structures of atomic clusters is a challenging issue.The difficulty also originates from the computational cost of ab initio methods for describing the potential energy surface.Recently,machine learning techniques have been widely utilized to accelerate materials discovery and molecular simulation.Compared to the commonly used artificial neural network,graph network is naturally suitable for clusters with flexible geometric environment of each atom.Herein we develop a cluster graph attention network(CGANet)by aggregating information of neighboring vertices and edges using attention mechanism,which can precisely predict the binding energy and force of silver clusters with root mean square error of 5.4 meV/atom and mean absolute error of 42.3 meV/Å,respectively.As a proof-of-concept,we have performed global optimization of mediumsized Agn clusters(n=14–26)by combining CGANet and genetic algorithm.The reported ground-state structures for n=14–21,have been successfully reproduced,while entirely new lowest-energy structures are obtained for n=22–26.In addition to the description of potential energy surface,the CGANet is also applied to predict the electronic properties of clusters,such as HOMO energy and HOMO-LUMO gap.With accuracy comparable to ab initio methods and acceleration by at least two orders of magnitude,CGANet holds great promise in global search of lowest-energy structures of large clusters and inverse design of functional clusters.

AN ANALYSIS ABOUT BEHAVIOR OF EVOLUTIONARY ALGORITHMS:A KIND OF THEORETICAL DESCRIPTION BASED ON GLOBAL RANDOM SEARCH METHODS

Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstruction and evolution of the sampling distributions over the space of candidate solutions. Iterativeconstruction of the sampling distributions is based on the idea of the global random search of generationalmethods. Under this frame, propontional selection is characterized as a gobal search operator, and recombination is characerized as the search process that exploits similarities. It is shown-that by properly constraining the search breadth of recombination operators, weak convergence of evolutionary algorithms to aglobal optimum can be ensured.

Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

Improvement of Pure Random Search in Global Optimization

In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some numerical experiments for the new stochastic global optimization algorithms are presented for a class of test problems.

ON THE GLOBAL CONVERGENCE OF CONJUGATE GRADIENT METHODS WITH INEXACT LINESEARCH

In this paper we consider the global convergence of any conjugate gradient method of the form d1=-g1,dk+1=-gk+1+βkdk(k≥1)with any βk satisfying sume conditions,and with the strong wolfe line search conditions.Under the convex assumption on the objective function,we preve the descenf property and the global convergence of this method.

GLOBAL CONVERGENCE OF THE GENERAL THREE TERM CONJUGATE GRADIENT METHODS WITH THE RELAXED STRONG WOLFE LINE SEARCH

The global convergence of the general three term conjugate gradient methods with the relaxed strong Wolfe line search is proved.

Global Convergence of an Extended Descent Algorithm without Line Search for Unconstrained Optimization

In this paper, we extend a descent algorithm without line search for solving unconstrained optimization problems. Under mild conditions, its global convergence is established. Further, we generalize the search direction to more general form, and also obtain the global convergence of corresponding algorithm. The numerical results illustrate that the new algorithm is effective.

The global convergence of the non-quasi-Newton methods with non-monotone line search

The non-quasi-Newton methods for unconstrained optimization was investigated. Non-monotone line search procedure is introduced, which is combined with the non-quasi-Newton family. Under the uniform convexity assumption on objective function, the global convergence of the non-quasi-Newton family was proved. Numerical experiments showed that the non-monotone line search was more effective.

Global Convergence of Curve Search Methods for Unconstrained Optimization

In this paper we propose a new family of curve search methods for unconstrained optimization problems, which are based on searching a new iterate along a curve through the current iterate at each iteration, while line search methods are based on finding a new iterate on a line starting from the current iterate at each iteration. The global convergence and linear convergence rate of these curve search methods are investigated under some mild conditions. Numerical results show that some curve search methods are stable and effective in solving some large scale minimization problems.

COSPAS-SARSAT系统第二代示位标信号的软件无线电设计实现

遇险示位标是全球卫星搜救系统(COSPAS-SARSAT)的一个重要组成部分。目前遇险示位标大多是第一代窄带示位标,采用固定的调制变频架构,存在集成度低、功能单一、升级需要更换硬件电路等问题。针对这些问题,文中设计了一种基于ZYNQ7000+AD9361软件定义无线电集成架构的第二代遇险信标示位标信号产生电路,其优点是信息内容、信号格式、发射参数可软件灵活定义。文中首先根据COSPAS-SARSAT系统第二代遇险示位标信号的格式规范,设计了扩频、滤波、调制等信号处理流程,仿真验证了信号特性;然后,用ZYNQ7000+AD9361软件定义无线电架构完成了PL和PS设计,实现了符合第二代COSPAS-SARSAT示位标规范的发射信号;最后进行了测试,结果表明该设计方法符合设计要求,有效可行。该设计具有分立器件少、集成度很高、高度可编程性、可灵活实现示位标的在线维护升级和信号转换等优点。

一种近似BFGS的自适应双参数共轭梯度法

为了更加有效的求解大规模无约束优化问题,本文基于自调比无记忆BFGS拟牛顿法,提出一个自适应双参数共轭梯度法,设计的搜索方向满足充分下降性,在一般假设和标准Wolfe线搜索准则下,证明该方法具有全局收敛性,数值实验结果证明提出的新算法是有效的.

基于改进A^(*)算法的AGV全局路径规划

采用八邻域搜索策略的传统A^(*)算法对AGV(自动导引车)进行全局路径规划时,存在搜索邻域过多、实时性差和生成路径拐点多等问题,本研究采用三邻域与八邻域混合搜索策略对传统A^(*)算法的搜索策略进行改进,改进后的A^(*)算法在当前搜索点周围不存在障碍物时,选取指向终点的三个栅格作为搜索邻域,当搜索点周围出现障碍物,则转换为传统的八邻域搜索,并在完成搜索后,对搜索路径进行拉直处理,消除多余拐点,减少路径长度。仿真实验结果表明,改进A^(*)搜索算法能有效缩短搜索时间、减少路径拐点数量并缩短路径长度,提高AGV运行效率。

基于多策略融合改进粒子群算法的路径规划研究

针对传统粒子群算法(particle swarm optimization,PSO)在路径规划中易陷入局部最优使得规划路径较长以及搜索后期由于种群多样性降低容易陷入停滞等问题,提出一种多策略融合粒子群算法(multi-strategy fusion particle swarm optimization,MFPSO)并将其应用于路径规划中。首先,利用中垂线算法(midperpendicular algorithm)的粒子位置更新方法提升粒子的收敛速度;其次,在最优粒子附近采用生成爆炸粒子的策略使算法跳出局部最优;然后,引入线性动态惯性权重调整方法,增加算法的搜索能力;最后,在路径规划应用中采用全局最优解局部搜索策略,在算法后期得出的最优路径再进行局部搜索得出更优的路径,增加机器人路径规划能力。仿真结果表明,多策略融合粒子群算法在路径规划中具有更高的路径搜索能力。

量子计算在地球物理学中的应用

量子计算具有强大的计算能力,被视为一种可能对未来产生颠覆性影响的计算方法,可为多种复杂计算问题的求解提供新思路,目前已在多学科或领域实践与应用。近年来,量子计算逐步应用于地球物理研究,多种量子算法及量子计算机的应用为揭示地球内部构造、探测深部资源等提供了技术支持。量子计算具备高效求解科学问题的能力,在地球物理领域具有巨大的应用潜力。为此,系统分析、阐述量子计算原理并总结了量子算法的发展现状,对其在地球物理数据采集、波场模拟、反问题求解等领域的成果进行归纳、分析;通过建立理论模型并进行反演,验证了量子算法的优越性;最后展望了量子计算未来可能的研究方向。

计算机兵棋系统空中航线搜索研究

空中航线搜索作为空中作战行动模拟的首要环节,搜索效率和精度直接影响实现效果。针对计算机兵棋系统使用Dijkstra算法进行空中航线搜索造成的运行效率低下和转弯点过多问题,引进D*Lite算法,通过改造航线启发距离提高搜索精度和效率,通过改造代价估计函数减少非用户设定航线转弯点数量,并通过仿真对比验证本文算法的有效性。实验结果表明,使用上述算法可在各类地图规模中稳定搜索到符合空中机动障碍约束且平滑度更高的最短航线,在全局静态环境下运算时间平均减少97.01%,在局部动态环境下运算时间平均减少99.15%。

一种WYL型谱共轭梯度法的全局收敛性

为解决大规模无约束优化问题,该文结合WYL共轭梯度法和谱共轭梯度法,给出了一种WYL型谱共轭梯度法.在不依赖于任何线搜索的条件下,该方法产生的搜索方向均满足充分下降性,且在强Wolfe线搜索下证明了该方法的全局收敛性.与WYL共轭梯度法的收敛性相比,WYL型谱共轭梯度法推广了线搜索中参数σ的取值范围.最后,相应的数值结果表明了该方法是有效的.

一种具有随机单向变异的基于小波框架的奇点集检测图像去噪算法

在图像恢复过程中,奇点集检测结果的准确性很大程度上会受到噪声的干扰,并且在其检测的迭代过程中易陷入局部最优。利用随机全局搜索的思想,借鉴遗传算法的变异操作,提出一种基于小波框架的具有随机单向变异操作的奇点集检测图像去噪算法,在保证图像恢复效果的同时,极大缩短了运算时间。最后通过实验验证了该算法的有效性。

A New Class of Nonlinear Conjugate Gradient Methods with Global Convergence Properties

非线性共轭梯度法由于其迭代简单和储存量小,且搜索方向不需要满足正割条件,在求解大规模无约束优化问题时占据及其重要的地位.提出了一类新的共轭梯度法,其搜索方向是目标函数的下降方向.若假设目标函数连续可微且梯度满足Lipschitz条件,线性搜索满足Wolfe原则,讨论了所设计算法的全局收敛性.

基于GA-BP神经网络的鲜食玉米产量预测

鲜食玉米因其营养丰富、用途广泛、市场潜力大等优势而备受关注,目前,我国鲜食玉米种植面积逐渐扩大,鲜食玉米产量的高效预测对制定其生长期间的精准管理决策具有重要意义。针对传统BP神经网络在预测中存在测试精度低、鲁棒性差等问题,利用遗传算法(Genetic Algorithm,GA)对BP神经网络模型进行优化,构建GA-BP神经网络模型。基于2010—2021年间田间物联网获取的气象因子(大气湿度、大气温度、降雨量)、田间水热因子及鲜食玉米实际产量,分别采用BP神经网络、GA-BP神经网络模型及粒子群优化算法(Particle Swarm Optimization,PSO)优化BP神经网络(PSO-BP)对所选地区鲜食玉米产量进行预测与相关性分析。结果表明,鲜食玉米产量与月最低土壤温度、月平均土壤温度、月大气最高温度和月平均大气湿度相关性极显著,相关系数高于0.8,与月最高温度、月土壤平均含水率、月大气平均温度、月降雨量相关性显著,与月大气最低温度相关性较弱。GA-BP神经网络模型精度明显高于PSO-BP及BP神经网络模型,R^(2)达到0.9564。因此,通过GA-BP神经网络模型可以更科学、合理地对鲜食玉米的产量进行预测,从而对鲜食玉米生产及管理措施的调整具有重要的指导意义。