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。     

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。     

浅议我国上市公司内部控制环境优化

控制环境是内部控制框架的基础,由于我国目前上市公司内部控制体系建设尚处于初级阶段,决定了其内部控制环境呈现出固有弊端.分析了我国上市公司在控制环境方面存在的缺陷,并提出了相应的优化措施,以期能对内部控制环境的完善起到一定的作用.     

基于中压加氢改质方案的炼油流程协同优化

中国石化北京燕山分公司(简称燕山分公司）为增产高附加值产品、提升效益，对炼油系统进行了流程协同优化。中压加氢裂化装置掺炼催化裂化柴油，由加氢裂化方案改为加氢改质方案运行，将改质柴油送入三号催化裂化装置（简称三催化装置）的提升管进行回炼；同时，将焦化蜡油改入加氢裂化装置进行加工，而蜡油加氢装置不再加工焦化蜡油以改善催化裂化原料。协同优化后，中压加氢改质装置的柴油产品十六烷值提高7个单位；三催化装置的液化气收率提高1.96百分点，汽油收率增加0.88百分点，总液体收率增加2.28百分点；高压加氢裂化装置喷气燃料产品的密度（20 ℃）降低至806 kg/m3，烟点为23.8 mm，尾油BMCI由11.8降低至10.8；蜡油加氢装置精制蜡油的饱和分质量分数提高4.68百分点，芳香分质量分数降低5.96百分点，氮质量分数降低0.06百分点，使催化裂化原料性质得以改善。通过将中压加氢改质装置的喷气燃料馏分抽出送催化裂化装置回炼，与回炼改质柴油相比，催化裂化汽油的研究法辛烷值（RON）增加1.0个单位，改质柴油十六烷值提高4.8个单位。通过全炼油板块系统性优化，燕山分公司车用柴油产品的十六烷值由53.5降低至51.5，解决了质量过剩问题。