水性聚氨酯材料配方设计技巧

介绍了优选法的0.618法和分数法,对其进行改进创新和原理证明,并应用于生产涂料配方设计中。     

中国石化集团原油资源优化配置及建议

介绍了石化集团原油资源现状，分析了原油资源配置过程中存在的主要问题，提出了资源优化配置的原则与基本思路，及实施原油资源优化配置的配套措施和建议。     

流程生产的可视化整合和多目标优化软件

针对现代企业所面临的挑战，开发了一个软件系统可以根据市场的动态变化和企业内部的设备状况，优化设备配置、优化原料和产品方案。采用可视化形式实施仿真运行和经济效益分析预测。     

邻甲苯基硫脲实验总结

在本文中，合成了邻甲苯基硫脲。优选出合成的较好条件，收率较文献介绍有所提高。     

MES在聚氯乙烯生产中的应用

介绍了MES在聚氯乙烯生产中乙炔站、氯乙烯、聚合各工序的生产平衡、实时参数及关联参数优化、辅助材料的核算等应用情况。     

UCC项目总承包工程建设的过程管理

由成都建筑材料工业设计研究院有限公司(简称成都院)和中国建材装备有限公司组成联合体总承包的UCC 10 000 t/d熟料水泥生产线,成都院负责提供合同范围内所有的工程设计、核心技术及核心设备、现场管理、人员培训、生产线达标考核,并具体负责实施和完成所有的土建及安装工程。在总承包工程建设的过程管理中,成都院以设计为灵魂,贯彻了"工艺流程顺畅,总图布局合理,系统配置先进可靠,合理节省投资"的设计理念;成都院克服了要求高、工期短、气候条件恶劣、国外施工人员管理难度大、功效低等重重困难,按时、保质保量、安全地完成了土建、安装、调试和试车任务;2007年4月26日,该生产线一次性通过业主的达标考核。我们的经验是:(1)有专业设计院全面介入的工程总承包是项目顺利进行的可靠保证;(2)参与建设的相关单位要相互信任、相互配合、目标一致;(3)工程建设管理科学化、人性化很重要。该项目的成功建设,使成都院总包工程建设的过程管理更加正规化、合理化和科学化。     

A multi-objective hyper-heuristic based on choice function

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.     

A hybrid algorithm based on particle swarm and chemical reaction optimization

In this paper, a hybrid method for optimization is proposed, which combines the two local search operators in chemical reaction optimization with global search ability of for global optimum. This hybrid technique incorporates concepts from chemical reaction optimization and particle swarm optimization, it creates new molecules (particles) either operations as found in chemical reaction optimization or mechanisms of particle swarm optimization. Moreover, some technical bound constraint handling has combined when the particle update in particle swarm optimization. The effects of model parameters like InterRate, γ, Inertia weight and others parameters on performance are investigated in this paper. The experimental results tested on a set of twenty-three benchmark functions show that a hybrid algorithm based on particle swarm and chemical reaction optimization can outperform chemical reaction optimization algorithm in most of the experiments. Experimental results also indicate average improvement and deviate over chemical reaction optimization in the most of experiments.     

Quadratic optimization fine tuning for the Support Vector Machines learning phase

This work presents a comparative analysis of specific, rather than general, mathematical programming implementation techniques of the quadratic optimization problem (QP) based on Support Vector Machines (SVM) learning process. Considering the Karush–Kuhn–Tucker (KKT) optimality conditions, we present a strategy of implementation of the SVM-QP following three classical approaches: (i) active set, also divided in primal and dual spaces, methods, (ii) interior point methods and (iii) linearization strategies. We also present the general extension to treat large-scale applications consisting in a general decomposition of the QP problem into smaller ones, conserving the exact solution approach. In the same manner, we propose a set of heuristics to take into account for a better than a random selection process for the initialization of the decomposition strategy. We compare the performances of the optimization strategies using some well-known benchmark databases.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.