基于半监督谱聚类的最优主动解列断面搜索

主动解列最优断面搜索是依据广域测量信息,在大电网遭受大扰动失步崩溃之前,依据实时工况和运行方式,快速准确求取电力孤岛划分的紧急策略。然而,在实际大系统的求解中,计算复杂度呈几何指数增长,是一个NP难题。提出了一种半监督谱聚类算法,首先采用最小复合有功潮流冲击的目标函数和机组同调/分离等相关约束构建详细解列断面搜索模型,然后将最优断面搜索的优化求解过程,映射为约束谱聚类对静态图分割的松弛解求取过程,最后通过改进的PAM聚类算法选择最优主动解列断面。上述过程,在不丢失全网信息前提下,降低了时间复杂度,IEEE 118标准算例和四川电网实际系统的仿真验证,证明了该算法的正确性、有效性和快速性。     

基于聚类混合遗传算法的LRP问题研究

在配送系统中,配送中心选址问题与在此基础上的车辆路径问题相互影响,属于NP-hard难题,为有效解决定位-车辆路径问题（LRP）,文中提出的两阶段算法设计结合了聚类算法和混合遗传是算法,针对实际情况在遗传算法中加入爬山算法,同时采用改进的自适应交叉、变异算法,确保种群的最优个体参与进化,提高了遗传算法的局部搜索能力。仿真实验表明,所改进的混合遗传算法有较好高的全局寻优能力,且其收敛速度快,是解决配送路径优化问题的有效方法。     

Two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times

Two-machine no-wait flowshop scheduling problems in which the processing time of a job is a function of its position in the sequence and its resource allocation are considered in the study. The primary objective is to find the optimal sequence of jobs and the optimal resource allocation separately. Here we propose two separate models: minimizing a cost function of makespan, total completion time, total absolute differences in completion times and total resource cost; minimizing a cost function of makespan, total waiting time, total absolute differences in waiting times and total resource cost. Since each model is strongly NP-hard, we solve both models by breaking them down to two sub-problems, the optimal resource allocation problem for any job sequence and the optimal sequence problem with its optimal resource allocation. Specially, we transform the second sub-problem into the minimum of the bipartite graph optimal matching problem (NP-hard), and solve it by using the classic KM (Kuhn–Munkres) algorithm. The solutions of the two sub-problems demonstrate that the target problems remain polynomial solvable under the proposed model.     

Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.     

基于启发式遗传算法求解加工时间可控单台机器

对NP—Hard的加工时间可控单机排序问题提出一种启发式遗传算法求解其次优解。在介绍该问题数学模型后,利用二维编码描述加工次序与加工时间,染色体的第一行用整数编码来表示工件加工次序,染色体第二行用实数编码表示实际加工时间。根据启发式知识定义了新的交叉与变异操作,这样不仅容易产生优良的初始解,而且缩小了搜索范围,提高了搜索速度和精度。算例仿真研究验证了提出算法的有效性。     

基于混沌遗传算法的测试选择优化问题研究

针对测试选择优化这一NP难解问题,文中利用改进的遗传算法对其进行优化.算法以遗传算法为主流程,利用混沌现象不重复遍历的特点优化生成初始种群,然后对每次迭代中的个体以一定的概率进行混沌优化.最后,以超外差接受器为例,对算法的有效性进行了验证.事实证明,该算法能够较快地搜索到优化问题的最优解,验证了混沌遗传算法对测试选择优化问题的有效性.     

Ant colony optimisation with parameterised search space for the job shop scheduling problem

The job-shop scheduling problem (JSSP) is known to be NP-hard. Due to its complexity, many metaheuristic algorithm approaches have arisen. Ant colony metaheuristic algorithm, lately proposed, has successful application to various combinatorial optimisation problems. In this study, an ant colony optimisation algorithm with parameterised search space is developed for JSSP with an objective of minimising makespan. The problem is modelled as a disjunctive graph where arcs connect only pairs of operations related rather than all operations are connected in pairs to mitigate the increase of the spatial complexity. The proposed algorithm is compared with a multiple colony ant algorithm using 20 benchmark problems. The results show that the proposed algorithm is very accurate by generating 12 optimal solutions out of 20 benchmark problems, and mean relative errors of the proposed and the multiple colony ant algorithms to the optimal solutions are 0.93% and 1.24%, respectively.     

Programs as Paradigms

How might we synthesise two very different approaches in architecture? One based on programme and another on typology or paradigm. Pablo Miranda Carranza , a researcher at the Architecture School at the RoyalInstitute of Technology (KTH) in Stockholm, takes his cue from an approach suggested by Colin Rowe in the early 1980s to examine how computation formulates architectural thinking and presentation.     

求解蛋白质结构问题的改进模拟退火算法*

将模拟退火(SA）思想用于求解蛋白质结构预测问题,并在此基础上提出了两个提高解的质量和加快收敛速度的改进策略,计算结果表明改进后的SA算法的计算效率优于目前常用的遗传算法和Monte Carlo方法。     

示例学习的扩张矩阵理论

本文提出示例学习的一种计算理论,扩张矩阵论.根据这个理论,示例学习中一些主要最优化问题被证明是NP难题,并给出这些难题的近似解法及下界的估计.