基于非线性选择方法的多目标优化遗传算法

针对排序选择法中广泛采用的线性选择方法的缺陷,提出了一种非线性选择方法。这种选择方法既充分体现了非劣解集对劣解集的优先选择权,又考虑到了非劣解集和劣解集中个体的平等性。理论分析和仿真计算表明,这种新的排序选择法不仅能得到分布广泛的Pareto最优解,而且进化速度极快,一般只需30-50代。

基于■2,1范数和神经网络的非线性特征选择方法

在基于■2,1范数的特征选择方法中,■2,1范数可以使选择的特征具有组间稀疏性和组内稀疏性,同时还可以去除特征数据中的异常值。然而,大多数基于■2,1范数的特征选择算法常通过线性方程求解,无法探究特征之间的非线性关系。因此,本文提出了一种基于■2,1范数的非线性特征选择方法,将■2,1范数与神经网络相结合。一方面,该方法利用神经网络的非线性特性对■2,1范数进行求解。另一方面,该方法利用■2,1范数实现基于神经网络框架的特征选择。最后,本文将该方法与当前流行的特征选择方法在八个公开数据集进行了对比,实验结果验证了该方法具有一定的优越性。

基于K-means的改进人工蜂群聚类算法

针对K-means聚类算法对初始聚类中心敏感和易陷入局部最优解的缺点,提出一种基于K-means的人工蜂群(ABC)聚类算法。将改进的人工蜂群算法和K-means迭代相结合,使算法对初始聚类中心的依赖性和陷入局部最优解的可能性降低,提高了算法的稳定性。通过基于反向学习的初始化策略,增强了初始群体的多样性。利用非线性选择策略,改善了过早收敛问题,提高了搜索效率。通过对邻域搜索范围的动态调整,提高了算法收敛速度,增强了局部寻优能力。实验结果表明,该算法不仅克服了K-means算法稳定性差的缺点,而且具有良好的性能和聚类效果。

一类分数阶微分包含耦合系统边值问题解的存在性

利用Kakutani映射非线性选择性定理研究了一类分数阶微分包含耦合系统边值问题解的存在性,结果表明当多值映射具有凸值时上述边值问题至少存在一个解的充分条件.

CoMP-transmission-based energy-efficient scheme selection algorithm for LTE-A systems

In order to achieve higher system energy efficiency （EE）,a new coordinated multipoint （CoMP）-transmission-based scheme selection energy saving （CTSES）algorithm is proposed for downlink homogeneous cellular networks.The problem is formulated as an optimization of maximizing system EE,under the constraints of the data rate requirement and the maximum transmit power.The problem is decomposed into power allocation and alternative scheme selection problems.Optimal power allocation is calculated for CoMP-JT （joint transmission）and CoMP-CS （coordinated scheduling） transmissions,and the scheme with higher EE is chosen. Since the optimal problem is a nonlinear fractional optimization problem for both CoMP transmission schemes, the problem is transformed into an equivalent problem using the parametric method. The optimal transmit power and optimal EE are obtained by an iteration algorithm in CoMP-JT and CoMP-CS schemes.Simulation results show that the proposed algorithm offers obvious energy-saving potential and outperforms the fixed CoMP transmission scheme.Under the condition of the same maximum transmit power limit,the empirical regularity of user distribution for scheme choice is presented, and using this regularity, the computational complexity can be reduced.

高阶中立型差分方程非振动解的存在性

得到了下列高阶中立型差分方程的一个非振动性理论,Δ[rn(Δm-1(xn+axn-τ))a]+f(n,xn-σ1,xn-σ2,…,xn-σu)=0,n∈N,其中α为正奇数的商,m,u为大于等于2的整数,rn>0,其中n∈N,a∈R,τ,σ1,σ2都为大于等于0的整数以及f∈C([n0,∞]×R×R×…×Ru,R)。

Study on Collision Between Two Ships Using Selected Parameters in Collision Simulation

In the present analysis, several parameters used in a numerical simulation are investigated in an integrated study to obtain their influence on the process and results of this simulation. The parameters studied are element formulation, friction coefficient, and material model. Numerical simulations using the non-linear finite element method are conducted to produce virtual experimental data for several collision scenarios. Pattern and size damages caused by collision in a real accident case are assumed as real experimental data, and these are used to validate the method. The element model study performed indicates that the Belytschko-Tsay element formulation should be recommended for use in virtual experiments. It is recommended that the real value of the friction coefficient for materials involved is applied in simulations. For the study of the material model, the application of materials with high yield strength is recommended for use in the side hull structure.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.

A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems

This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper. This approach offers promise for the portfolio problems in practice.

Portfolio Selection Model with Derivative Securities

Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is developed based on utility function which can capture asymmetries in random variable distributions. Other realistic conditions are also considered, such as liabilities and integer decision variables. Since the resulting model is a complex mixed integer nonlinear programming problem, simulated annealing algorithm is applied for its solution. A numerical example is given and sensitivity analysis is conducted for the model.

A Nonlinear Interval Portfolio Selection Model and Its Application in Banks

In classical Markowitz＇s Mean-Variance model, parameters such as the mean and covari- ance of the underlying assets＇ future return are assumed to be known exactly. However, this is not always the case. The parameters often correspond to quantities that fall within a range, or can be known ambiguously at the time when investment decision must be made. In such situations, investors determine returns on investment and risks etc. and make portfolio decisions based on experience and economic wisdom. This paper tries to use the concept of interval numbers in the fuzzy set theory to extend the classical mean-variance portfolio selection model to a mean-downside semi-variance model with consideration of liquidity requirements of a bank. The semi-variance constraint is employed to control the downside risk, filling in the existing interval portfolio optimization model based on the linear semi-absolute deviation to depict the downside risk. Simulation results show that the model behaves robustly for risky assets with highest or lowest mean historical rate of return and the optimal investment proportions have good stability. This suggests that for these kinds of assets the model can reduce the risk of high deviation caused by the deviation in the decision maker＇s experience and economic wisdom.