Improving the non-dominated sorting genetic algorithm using a gene-therapy method for multi-objective optimization

The non-dominate sorting genetic algorithmic-II (NSGA-II) is an effective algorithm for finding Pareto-optimal front for multi-objective optimization problems. To further enhance the advantage of the NSGA-II, this study proposes an evaluative-NSGA-II (E-NSGA-II) in which a novel gene-therapy method incorporates into the crossover operation to retain superior schema patterns in evolutionary population and enhance its solution capability. The merit of each select gene in a crossover chromosome is estimated by exchanging the therapeutic genes in both mating chromosomes and observing their fitness differentiation. Hence, the evaluative crossover operation can generate effective genomes based on the gene merit without explicitly analyzing the solution space. Experiments for nine unconstrained multi-objective benchmarks and four constrained problems show that E-NSGA-II can find Pareto-optimal solutions in all test cases with better convergence and diversity qualities than several existing algorithms.     

Robust linear optimization under matrix completion

Linear programming models have been widely used in input-output analysis for analyzing the interdependence of industries in economics and in environmental science.In these applications,some of the entries of the coefficient matrix cannot be measured physically or there exists sampling errors.However,the coefficient matrix can often be low-rank.We characterize the robust counterpart of these types of linear programming problems with uncertainty set described by the nuclear norm.Simulations for the input-output analysis show that the new paradigm can be helpful.     

Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.     

基于ANP的装备质量管理风险评估模型

根据装备质量管理的特点对装备质量管理的风险因素进行了分析,依据指标体系的设计原则建立了装备质量管理风险评估的指标体系,并给出了基于网络层次分析法和粒子群优化算法的的装备质量管理风险评估指标权重值的确定方法.并给出了实例分析.     

错误评价及其消错优化研究

现实中决策者往往希望将评价对象的特征值与期望值之间的偏差控制在可接受范围之内,而且偏差越小越好.基于消错学对错误的界定,利用错误函数对评价对象的特征指标值与评价标准之间的偏差进行了度量,并遵循"区分主次、明确底线、综合集成"的思路,通过建立消错规划模型实施消错优化,从而在保证各特征指标不突破临界错误值的情况下,做到评价对象的整体错误值最小化.最后通过算例分析,演示了消错优化的过程,验证了消错优化模型的合理性和可操作性.     

Hilbert空间中的不可微最优化问题的基路径增量目标水平算法

主要研究了Hilbert空间中的一类不可微最优化问题的基路径增量目标水平算法.证明了当约束集合为有界集时,问题的最优解集非空,且这时通过算法生成的迭代点列是弱收敛于最优解的..     

基于Gristensen算法的砂岩储层横波速度计算方法

由于横波测量成本高,大部分油田测井资料中仅有少部分横波数据,则依据常规测井资料准确构建横波曲线已成为关键.考虑到横波构建理论方法更多地依赖实验室分析资料,提出在Gristensen经验公式基础上,以研究区横波测量曲线为约束,通过优化反演确立公式系数是一种好的研究思路.优化方法实现中采用阻尼型高斯牛顿法,保障了多参数优化迭代的收敛性和精度.将研究方法用于卫星油田横波数据构建中,取得好的计算结果.     

改进的多目标粒子群算法

提出了一个改进的粒子群算法并将其用于解决多目标优化问题．该算法利用粒子群算法的信息传递机制，引入多目标演化算法常用的归档技术，采用SPEA2算法的环境选择和配对选择策略，使得整个群体在保持适当的选择压力的情况下收敛于Pareto最优解集．标准测试函数的数值实验结果表明，所提出的算法能够使找到的解集快速收敛到Pareto非劣最优目标域，并且解集沿着Pareto非劣最优目标域有很好的扩展性．