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 .

a backtracking search tool for constructing combinatorial test suites

Combinatorial testing is an important testing method. It requires the test cases to cover various combinations of parameters of the system under test. The test generation problem for combinatorial testing can be modeled as constructing a matrix which has certain properties. This paper first discusses two combinatorial testing criteria: covering array and orthogonal array, and then proposes a backtracking search algorithm to construct matrices satisfying them. Several search heuristics and symmetry breaking techniques are used to reduce the search time. This paper also introduces some techniques to generate large covering array instances from smaller ones. All the techniques have been implemented in a tool called EXACT (EXhaustive seArch of Combinatorial Test suites). A new optimal covering array is found by this tool.

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.