Model-driven visualizations of constraint-based local search

Visualization is often invaluable to understand the behavior of optimization algorithms, identify their bottlenecks or pathological behaviors, and suggest remedial techniques. Yet developing visualizations is often a tedious activity requiring significant time and expertise. This paper presents a framework for the visualization of constraint-based local search (CBLS) algorithms. Given a high-level model and a declarative visualization specification, the CBLS visualizer systematically produces animations to visualize constraints and objectives, violations, and conflicts, as well as the temporal behavior of these measures. The visualization specification is declarative and typically composed of a triple (what,where,how) indicating what to display, where, and with which graphical objects. The visualizer architecture is compositional and extensible. It provides building blocks which can be assembled freely by the user and focuses almost exclusively on static aspects, the dynamic aspects being automated by the use of invariants. The paper highlights various functionalities of the visualizer and describes a blueprint for its implementation.     

Container loading with multi-drop constraints

In this paper, an algorithm for the container-loading problem (CLP) with multi-drop constraints is presented. When adding multi-drop constraints, we demand that the relevant boxes must be available, without rearranging others, when each drop-off point is reached. To make the solutions feasible in the real world, it is further demanded that all boxes are placed in a feasible manner with respect to load-bearing strength and with proper support from below. This makes it possible to load consignments originating from builder merchants. A heuristic based on a tree search framework is proposed. It uses greedy solutions to evaluate each choice taken. To make the framework more generic, a dynamic breadth is proposed. Based on problem characteristics and the time limit imposed, it will choose the breadth of the tree, making sure that the time is utilised most profitably. The algorithm is tested on new real-world data from a Danish company distributing construction products. For the solutions to these problems to be feasible in a real-world setting, both multi-drop and load-bearing strength constraints are essential. The obtained results show that the proposed model and algorithm are able to solve the new real-world problems in fractions of a second. Furthermore, results obtained on benchmark problems indicate that the algorithm performs comparably with other more specialised methods.     

Development and optimization of ME-TVC desalination system

Significant and rapid developments have taken place recently in the multi-effect thermal vapor compression (ME-TVC) desalination system of SIDEM Company, particularly in enlarging the unit capacity. The new trend of combining ME-TVC with conventional multi-effect has allowed this unit capacity to increase from two to eight million imperial gallons per day (MIGD) in the last decade. This considerable increase in capacity, poses a real competition to the multi stage flash system (MSF) as a large-scale production plant with lower operation temperatures.A steady state mathematical model of the ME-TVC desalination system is developed in this paper using Engineering Equations Solver (EES) to evaluate the model system performance. The model validity is examined against three commercial ME-TVC units which showed good results. The main improvements in the performance during the past ten years are also outlined and discussed. Another purpose of this paper is to determine the optimum operating and design conditions of the ME-TVC desalination system through mathematical modeling optimization. A MATLAB algorithm solution is developed and used to solve model equations, where a different number of effects were tested to maximize the gain ratio using (1) Smart Exhaustive Search Method and (2) Sequential Quadratic Programming. Results showed that the maximum gain ratio varied between 8.5 and 18.5 for 4 and 12 effects with the optimal top brine temperature ranging between 55.8 and 67.5 °C and a reasonable specific heat transfer area. The optimal ranges of compression and entrainment ratios are between 1.81 to 3.68 and 0.73 to 1.65 respectively. The optimal results of 4-effect TVC unit are also compared with three commercial 4-effect units having almost the same input, which showed that further improvement in the distillate output production, compression and entrainment ratio can be achieved by combining the ME-TVC system with conventional multi-effect unit.     

基于元搜索的结果融合模型的改进与实现

针对元搜索引擎返回结果无序的问题,提出了运用特征模板的模糊积分算法来辅助实现文档评价的排序,以“个性化旅游服务平台”为实验基础平台,运用元搜索与垂直搜索相结合的方法,验证了该种改进后的结果融合模型的可行性及其优势。实验结果表明,该方法既能很好地体现文档的行业特性,又能很好地体现文档中特征向量与特征向量之间的相互关系对文档评价的影响。     

Tank模型及其在边坡水位预测中的应用

 Tank模型可以模拟非线性的降雨–地下水运移过程,并且能迅速得到解答。基于现有的单列tank模型,提出新的复合水箱模型。由于新模型参数超过20个,应用传统优化算法难以快速找到最优解,一种新的启发式自搜索算法(变维数搜索算法)被引入并改进后用于模型最优解的寻找。变维数搜索算法能够根据搜索进程的变化自动改变搜索维数并且快速找到最优解。27个参数的复合tank模型被应用于日本国道九号线的一个边坡,计算结果表明：变维数搜索算法能够在10 min左右找到合适的最优解;降雨过程复合tank模型计算的地下水位变化和观测值非常接近。最后通过和有限单元法计算结果的比较表明,有限单元法的计算结果受地质渗透特性的影响很大,而复合tank模型不存在这种问题。工程实例计算表明,该方法和监测结果比较一致,但其适应性更强,特别适用于没有进行足够地质结构探查的边坡。它能够快速反映降雨过程中地下水位的运移过程,可以推广使用。     

基于禁忌搜索的平行机多工厂供应链调度

提出一类平行机多工厂供应链调度问题,从整体的角度优化订单分配、生产调度和运输调度。以交货期限内完成所有工件的生产和运输总成本最小化为目标,构建了问题的混合整数规划模型。设计了一种基于向量组编码结构的禁忌搜索算法以求解该问题。使用供应链调度策略和独立决策策略分别对算例进行求解,数据结果验证了供应链调度策略的优越性。禁忌搜索算法结果与CPLEX的求解结果对比表明,禁忌搜索算法有效可行。     

改进遗传算法在电力系统无功优化中的应用

针对常规遗传算法（GA）的不足,提出了一种改进搜索策略的遗传算法,采取了以下改进措施：在遗传迭代中,根据种群进化过程的个体的适应值大小,对群体进行分级,对级别高的个体进行小范围的搜索,对级别低的个体在大范围内进行搜索,保证群体的多样性的同时,又留住了优良个体。将改进的遗传算法应用于电力系统无功优化,并与常规遗传算法进行了比较,结果表明改进算法在计算速度、收敛性和全局最优搜索能力都有提高。     

基于自由搜索的水库入库含沙量预测模型

水库泥沙淤积问题直接关系到水库的规模、寿命以及综合经济效益的发挥。入库水流含沙量是影响水库调度运行以及水库泥沙淤积的主要因素,目前国内结构简单、实用性较强的含沙量预测模型不多。以水库的入库站流量及上游站至入库站的区间流量为研究对象,提出了基于两个水文站流量的水库入库含沙量的预测模型,采用改进的自由搜索算法率定模型参数,并将模型应用于龚嘴水库的入库含沙量的预测。研究结果表明,构建的模型结构简单,参数率定便捷,预测含沙量的精度较高,为水库入库含沙量预测提供了一种简便适用的方法。     

美国专利商标局(USPTO)专利检索方法与评价

通过对美国专利商标局（USPTO）专利数据库检索过程的实例说明，介绍该专利网络检索方法，供用户在检索美国专利文献时参考与使用。     

Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. The honey-bees mating process may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bees mating. In this paper, the honey-bees mating optimization algorithm (HBMO) is presented and tested with few benchmark examples consisting of highly non-linear constrained and/or unconstrained real-valued mathematical models. The performance of the algorithm is quite comparable with the results of the well-developed genetic algorithm. The HBMO algorithm is also applied to the operation of a single reservoir with 60 periods with the objective of minimizing the total square deviation from target demands. Results obtained are promising and compare well with the results of other well-known heuristic approaches.