An Integrated Evolutionary Algorithm for Expensive Global Optimization

We propose an integrated algorithm named low dimensional simplex evolution extension (LDSEE) for expensive global optimization in which only a very limited number of function evaluations is allowed. The new algorithm accelerates an existing global optimization, low dimensional simplex evolution (LDSE), by using radial basis function (RBF) interpolation and tabu search. Different from other expensive global optimization methods, LDSEE integrates the RBF interpolation and tabu search with the LDSE algorithm rather than just calling existing global optimization algorithms as subroutines. As a result, it can keep a good balance between the model approximation and the global search. Meanwhile it is self-contained. It does not rely on other GO algorithms and is very easy to use. Numerical results show that it is a competitive alternative for expensive global optimization.

带赋值符号迁移图的局部优化算法

National Natural Science Foundation of China; Public Administration and Civil Service Bureau of Macau SAR; Companhia de Telecomunicacoes de Macau S.A.R.L.; Macau SAR Government Tourist Office

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.

基于代理模型的水下滑翔机机翼设计优化方法

在复杂工程系统的概念设计优化中,高精度数值分析方法得到广泛应用,将高精度数值分析与优化方法有机结合,对设计空间展开全面搜索与寻优,已经成为现代设计优化方法的重要发展方向。以水下滑翔机的概念设计为研究对象,引入代理模型,控制高精度分析试验的数量,有效地化解精度与效率之间的矛盾。将参数化几何建模、网格划分以及流体数值模拟分析集成为自动分析流程,并以此为基础采用试验设计理论,构建代理模型,解决水下滑翔机机翼的多目标设计优化问题。给出基于代理模型的设计优化过程,并系统地比较几种试验设计方法的适应性,重点讨论多项式响应面和径向基函数代理模型,所得代理模型相对误差小于2%。采用梯度寻优方法与遗传算法在给定设计空间内进行全面搜索,获得机翼的最佳平面构形,水下滑翔机的升阻比提高6.76%,俯仰力矩的绝对值由0.2760N•m降低为0.0015N•m,提高水下滑翔机的运动性能。

基于遗传算法的单机提前/拖期调度方法研究

针对对工件有不同交货期要求 ,并对提前 /拖期工件进行惩罚的一类单机调度问题 ,提出了基于遗传算法的优化方法 .提出一种基于“非”一致次序交叉算子的遗传算法 ,用于排序优化 ;在分析了惩罚函数性质的基础上 ,给出了最优开工时间算法 .对不同规模的调度问题 ,应用本文提出的算法与其它算法进行了比较 ,结果表明该方法具有优良的性能 .

Optimization of positions and currents of tokamak poloidal field coils using genetic algorithms

Plasma equilibrium geometry has a great influence on the confinement and magnetohydrodynamic stability in tokamaks. The poloidal field (PF) system of a tokamak should be optimized to support the prescribed plasma equilibrium geometry. In this paper, a genetic algorithm-based method is applied to solve the optimization of the positions and currents of tokamak PF coils. To achieve this goal, we first describe the free-boundary code EQT Based on the EQT code, a genetic algorithm-based method is introduced to the optimization. We apply this new method to the PF system design of the fusion-driven subcritical system and plasma equilibrium geometry optimization of the Experimental Advanced Superconducting Tokamak (EAST). The results indicate that the optimization of the plasma equilibrium geometry can be improved by using this method.

A scheme for multiple sequences alignment optimization-an improvement based on family representative mechanics features

As a basic tool of modern biology, sequence alignment can provide us useful information in fold, function, and active site of protein. For many cases, the increased quality of sequence alignment means a better performance. The motivation of present work is to increase ability of the existing scoring scheme/algorithm by considering residue–residue correlations better. Based on a coarse-grained approach, the hydrophobic force between each pair of residues is written out from protein sequence. It results in the construction of an intramolecular hydrophobic force network that describes the whole residue–residue interactions of each protein molecule, and characterizes protein's biological properties in the hydrophobic aspect. A former work has suggested that such network can characterize the top weighted feature regarding hydrophobicity. Moreover, for each homologous protein of a family, the corresponding network shares some common and representative family characters that eventually govern the conservation of biological properties during protein evolution. In present work, we score such family representative characters of a protein by the deviation of its intramolecular hydrophobic force network from that of background. Such score can assist the existing scoring schemes/algorithms, and boost up the ability of multiple sequences alignment, e.g. achieving a prominent increase (50%) in searching the structurally alike residue segments at a low identity level. As the theoretical basis is different, the present scheme can assist most existing algorithms, and improve their efficiency remarkably.

The Application of Toolkit of Optimization in Designs of Accelerator Magnets

The traditional design of accelerator magnet usually involves many time consuming iterations of the manual analysis process. A software platform to do these iterations automatically is proposed in this paper. In this platform, we use DAKOTA (a open source software developed by Sandia National Laboratories) as the optimizing routine, which provides a variety of optimization methods and algorithms, and OPERA (software from Vector Fields) is selected as the electromagnetic simulating routine. In this paper, two examples of designs of accelerator magnets are used to illustrate how an optimization algorithm is chosen and the platform works.