A multi-start central force optimization for global optimization

Central force optimization (CFO) is an efficient and powerful population-based intelligence algorithm for optimization problems. CFO is deterministic in nature, unlike the most widely used metaheuristics. CFO, however, is not completely free from the problems of premature convergence. One way to overcome local optimality is to utilize the multi-start strategy. By combining the respective advantages of CFO and the multi-start strategy, a multi-start central force optimization (MCFO) algorithm is proposed in this paper. The performance of the MCFO approach is evaluated on a comprehensive set of benchmark functions. The experimental results demonstrate that MCFO not only saves the computational cost, but also performs better than some state-of-the-art CFO algorithms. MCFO is also compared with representative evolutionary algorithms. The results show that MCFO is highly competitive, achieving promising performance.     

Optimal scheduling of single stage batch plants with direct heat integration

This paper addresses the multi-objective optimization problem arising in the operation of heat integrated batch plants, where makespan and utility consumption are the two conflicting objectives. A new continuous-time MILP formulation with general precedence variables is proposed to simultaneously handle decisions related to timing, product sequencing, heat exchanger matches (selected from a two-stage superstructure) and their heat loads. It features a complex set of timing constraints to synchronize heating and cooling tasks, derived from Generalized Disjunctive Programming. Through the solution of an industrial case study from a vegetable oil refinery, we show that major savings in utilities can be achieved while generating the set of Pareto optimal solutions through the ɛ-constraint method.     

金属配合物与核酸作用机理的理论计算

通过计算机模拟金属配合物与核酸作用机理,对其理论模型能量计算和结构优化。在不同的化学环境条件下得到不同的实验模拟结果,对真实的化学反应过程有重要的指导意义和参考意义。通过Gaussian03,Gauss View等软件的结合应用,分别应用于优化计算和理论建模,熟悉了对化学软件操作应用。     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

动态高压微射流处理对方竹笋膳食纤维理化及结构特性的影响

以方竹笋中提取的膳食纤维为研究对象，采用动态高压微射流（dynamic high-pressure micro-fluidization, DHPM）在不同压力条件（0，50，100，150，200 MPa）下进行处理，探究其对竹笋膳食纤维（bamboo shoots dietary fiber, BSDF）理化和结构特性的影响。结果表明，随着处理压力的增大，BSDF粒径先增大后减小，当处理压力为150 MPa时，粒径最小，为（370±11） nm，此条件下BSDF的持水力、持油力和膨胀力达到最大，较对照组分别提高了47.74%，50.54%，61.27%，且差异显著（P<0.05）。红外光谱分析表明DHPM处理不会改变BSDF的官能团，但会使BSDF内部的部分氢键断裂和半纤维素、木质素等发生降解；X射线衍射和热重分析表明DHPM处理不会引起BSDF的晶体结构改变，但晶体有序度会下降，进而导致其热稳定性降低；微观结构分析显示DHPM处理会使BSDF颗粒尺寸减小、表面粗糙、组织松散，且当处理压力为200 MPa时，颗粒发生团聚。综上，DHPM可以有效改善BSDF的理化性质，在膳食纤维改性方面具有一定的应用价值。