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。     

S6-11综放工作面回风巷支护设计优化

针对常村矿回采巷道变形严重,影响正常生产的问题,通过对矿井赋存条件、巷道现有支护形式和围岩变形过程分析,采用新型联合支护技术对S6-11回风巷道支护进行优化。现场实测巷道顶底板相对移近量最大120mm,两帮相对移近量最大140mm,均在允许正常范围之内。     

某污水处理厂MBR膜系统改造与运行管理

某污水处理厂设计规模为10万t/d,采用AAO+MBR工艺,设计出水水质满足《城镇污水处理厂污染物排放标准》(GB 18918-2002)一级A标准。由于运行时间较长,MBR运行系统存在产能下降、膜丝断丝脱落、污堵和清洗频次以及强度增加等问题,严重影响正常生产运行。针对MBR系统运行中存在的问题,文中提出具体改造建议,并总结运行经验,进一步优化MBR膜系统的运行维护管理,为其他污水厂MBR膜系统运行问题提供解决思路和维护管理经验。     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

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。     

某轻型载货车储气罐支架振动断裂分析与优化

为了有效解决某轻型载货车储气罐支架的断裂故障,首先基于建立的储气罐支架有限元模型进行振动特性分析,分析结果表明其前三阶固有频率接均处于发动机激励频率范围之外,不会产生共振。其次测试各种道路的时域载荷,测试结果表明其中角度搓板路的激励频率与储气罐支架第一阶固有频率相接近,从而引起共振,不满足振动特性要求。然后对其进行振动强度分析,分析结果表明其应力水平不达标,其最大应力点与开裂处一致。再对其进行振动疲劳寿命预测分析,分析结果表明其疲劳寿命也不达标,其危险点也与失效位置相同。再采用集成平台对储气罐支架的结构进行优化设计,优化之后其模态频率、振动强度和振动疲劳均符合性能要求,并且其重量也有所减轻,总体优化效果较佳。最后整车试验结果表明优化之后储气罐支架的振动大幅度降低,并且没有发生失效。