An Opposition-Based Learning CRO Algorithm for Solving the Shortest Common Supersequence Problem

As a non-deterministic polynomial hard (NP-hard) problem, the shortest common supersequence (SCS) problem is normally solved by heuristic or metaheuristic algorithms. One type of metaheuristic algorithms that has relatively good performance for solving SCS problems is the chemical reaction optimization (CRO) algorithm. Several CRO-based proposals exist; however, they face such problems as unstable molecular population quality, uneven distribution, and local optimum (premature) solutions. To overcome these problems, we propose a new approach for the search mechanism of CRO-based algorithms. It combines the opposition-based learning (OBL) mechanism with the previously studied improved chemical reaction optimization (IMCRO) algorithm. This upgraded version is dubbed OBLIMCRO. In its initialization phase, the opposite population is constructed from a random population based on OBL; then, the initial population is generated by selecting molecules with the lowest potential energy from the random and opposite populations. In the iterative phase, reaction operators create new molecules, where the final population update is performed. Experiments show that the average running time of OBLIMCRO is more than 50% less than the average running time of CRO_SCS and its baseline algorithm, IMCRO, for the desoxyribonucleic acid (DNA) and protein datasets.     

基于启发式算法和支持向量机算法预测醛类化合物急性毒性

应用启发式算法(HM)和支持向量机算法(SVM)建立了88种醛类化合物对大鼠急性毒性的定量构效关系模型。应用ChemOffice2004软件进行化合物的结构输入,利用半经验方法进行分子结构优化,在CODDESA软件中计算出组成、拓扑、几何、电子和量子化学参数。并用启发式方法筛选出相对阳性电荷、氧原子数量、碳原子的最小亲核反应指数、双键相对数量、碳原子数量、碳氢键之间的最大交换能量、最大σ-σ键序和双键数量8个参数,在此基础上应该多元线性回归和支持向量机方法建立QSPR模型。两种方法均得到了较好的结果,HM和SVM的交互检验的相关系数分别为0.90和0.93;通过对模型的稳定性和预测能力比较,SVM建立的QSAR模型能够更好地预测醛类化合物对大鼠急性毒性LD50。     

A Soar-Based Space Exploration Algorithm for Mobile Robots

Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models the space exploration problem of mobile robots based on the decision-making process of the cognitive architecture of Soar, and three space exploration heuristic algorithms (HAs) are further proposed based on the model to improve the exploration speed of the robot. Experiments are carried out based on the Easter environment, and the results show that HAs have improved the exploration speed of the Easter robot at least 2.04 times of the original algorithm in Easter, verifying the effectiveness of the proposed robot space exploration strategy and the corresponding HAs.     

旅行推销员问题的算法综述

本文综述了旅行推销员问题 (TSP)近几十年来的算法研究进展 ,给出了一些主要算法的求解思想及其时间复杂度     

分子亲脂-亲水性的量子化学描述II. 氨基酸侧链的亲水指标和亲脂指标

在启发式亲脂势HMLP(heuristicmolecularlipophilicitypotential)的基础上提出了分子、分子片段和原子的亲水指标和亲脂指标.计算出了20个天然氨基酸侧链的亲水、亲脂指标和亲水、亲脂表面积,并用线性自由能函数表达氨基酸侧链的溶剂化自由能,?Gsol,=b0 b1Li b2Hi b3Si b4Si.应用线性自由能函数和氨基酸侧链的亲水和亲脂! -i指标,计算了20个氨基酸残基的3种相转移自由能(蒸气-水、蒸气-正辛醇、正辛醇-水)和正辛醇-水分配系数logPow,取得了与实验值高度一致的良好效果.HMLP的亲水和亲脂指标是HMLP的指标化,扩展了这一方法的使用范围.氨基酸侧链的亲水、亲脂指标和线性自由能函数有望用于生物大分子受体与配体的结合自由能的估算、蛋白质的结构与功能、蛋白-蛋白相互作用和识别的研究.     

唇形科植物挥发油化学成分的GC/MS研究

在运用GC/MS技术的基础上采用HELP(直观推导式演进特征投影法)方法研究了九种唇形科植物的化学成分, 并以唇形科植物半枝莲为例详细介绍了HELP的解析过程. 应用总体积积分法测定各成分的相对百分含量. 鉴定出相对共有成分达70余种, 大多数为萜类化合物及其衍生物. 不同的唇形科植物的挥发油化学成分与特征成分有明显差异. 九种药材挥发性成分中均含有桉油精(Eucalyptol, 含量0.10%～1.01%)和芳樟醇(Linalool, 0.11%～3.05%). 利用GC/MS分析法结合化学计量学分辨方法鉴定挥发油化学成分, 比单独使用GC-MS法结果更准确、可靠.     

多元曲线分辨结合在线红外用于3-氨基-4-氨基肟基呋咱的合成反应机理

利用红外光谱在线监测丙二睛、亚硝酸钠和盐酸羟胺合成3-氨基-4-氨基肟基呋咱的反应过程,采用多元曲线分辨-交替最小二乘法（MCR-ALS）、直观推导式演进特征投影法(HELP)等化学计量学方法对反应过程所获得的实时红外光谱数据矩阵进行解析,得到了各组分纯物质的浓度变化曲线和对应的红外光谱,并将多元曲线分辨 交替最小二乘法与直观推导式演进特征投影法的分析结果进行比较,得出可相互验证的一致结论,据此推出该反应合理的反应机理。 2种方法得到的反应物与生成物的光谱与原光谱的相似度近似于1,说明该解析方法具有准确性和可靠性。 结果表明,化学计量学结合红外光谱可有效的应用于3-氨基-4-氨基肟基呋咱合成过程的机理推断。     

Greedy Randomized Adaptive Search Procedures

Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.     

自由作业的供应链排序研究

研究一类集成工件加工和发送的供应链排序模型,即研究如何安排工件在自由作业机器上加工,把加工完毕的工件分批发送给下游客户,使得含生产排序费用和发送费用的目标函数最优.这里,分别取工件最大送到时间和平均送到时间为生产排序费用;而发送费用是由固定费用和与运输路径有关的变化费用组成.利用排序理论和动态规划方法,构造了自由作业供应链排序问题的多项式时间近似算法,并分析算法的性能比.     

基于贪婪禁忌搜索算法的垂岸式堆场出口箱装船翻箱研究

基于垂岸式自动化集装箱码头不同装船周期出口集装箱堆场多贝位混合堆存、场桥大车在贝位间频繁移动取箱装船特点,考虑装船发箱时场桥移动等操作时间及翻箱取箱次数对出口箱装船效率和连续性影响,建立多贝位出口箱装船堆场翻箱模型,提出两阶段贪婪禁忌搜索算法,将翻箱规则嵌入算法中,有效限制算法时间和解空间增长速度。通过算例,将提出的翻箱规则与现有常见翻箱规则进行对比,验证模型及算法的有效性与实用性。结果表明,提出的模型和算法可以在合理的求解时间内输出较优的翻箱方案,减少装船时场桥发箱作业时间,提高装船作业效率。